解释下面代码的作用：" def __len__(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1"

解释代码： def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

As an AI language model, I do not have emotions or personal opinions. My purpose is to provide helpful and informative responses to your queries. Could you please provide more context or a specific ...

解释代码 def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

As an AI language model, I do not have emotions or feelings. Therefore, I am not capable of experiencing or expressing the concept of "null." However, in computer science, "null" is a value that ...

from torch.utils.data import Dataset, DataLoader class IMDBDataset(Dataset): def init(self, data, labels, remain_length=True): if remain_length: self.data = tokenizer.decode(data, remove_bos=False, remove_eos=False, remove_pad=False) else: # 缩减一下数据 self.data = tokenizer.decode(data) self.labels = labels def getitem(self, index): text = self.data[index] label = self.labels[index] return text, label def len(self): return len(self.data) def collate_fct(batch): text_list = [item[0].split() for item in batch] label_list = [item[1] for item in batch] # 这里使用 padding first text_list = tokenizer.encode(text_list, padding_first=True).to(dtype=torch.int) return text_list, torch.tensor(label_list).reshape(-1, 1).to(dtype=torch.float) # 用RNN，缩短序列长度 train_ds = IMDBDataset(train_data, train_labels, remain_length=False) test_ds = IMDBDataset(test_data, test_labels, remain_length=False) 这段代码有什么用

需要解释Dataset和DataLoader的作用，以及collate_fn的自定义处理如何影响数据批次的组织。同时，提到RNN使用缩短序列的原因，比如处理长序列的梯度问题或计算效率。这段代码构建了一个基于PyTorch的文本分类数据...

import torch import torch.nn as nn import torch.optim as optim import pandas as pd from rdkit import Chem from rdkit.Chem import Draw, rdMolDescriptors from torch_geometric.data import Data, Batch from torch_geometric.nn import GCNConv, global_mean_pool, GATConv, GraphConv from torch_geometric.loader import DataLoader import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler import os import numpy as np import random from functools import lru_cache from torch.cuda import amp # 设置随机种子以确保结果可复现 def set_seed(seed): torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False set_seed(42) # ------------------------- 工具函数 ------------------------- def validate_atomic_nums(atomic_nums): valid_atoms = {1, 6, 7, 8, 16, 9, 17} # H, C, N, O, S, F, Cl if isinstance(atomic_nums, torch.Tensor): atomic_nums = atomic_nums.cpu().numpy() if atomic_nums.is_cuda else atomic_nums.numpy() if isinstance(atomic_nums, list): atomic_nums = np.array(atomic_nums) if atomic_nums.ndim > 1: atomic_nums = atomic_nums.flatten() atomic_nums = np.round(atomic_nums).astype(int) return [num if num in valid_atoms else 6 for num in atomic_nums] def smiles_to_graph(smiles): mol = Chem.MolFromSmiles(smiles) if mol is None: return None Chem.SanitizeMol(mol) atom_feats = [atom.GetAtomicNum() for atom in mol.GetAtoms()] edge_index = [] for bond in mol.GetBonds(): i, j = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() edge_index += [(i, j), (j, i)] x = torch.tensor(atom_feats, dtype=torch.long).view(-1, 1) edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous() return Data(x=x, edge_index=edge_index) def augment_mol(smiles): mol = Chem.MolFromSmiles(smiles) if mol: try: mol = Chem.AddHs(mol) except: pass return Chem.MolToSmiles(mol) return smiles # ------------------------- 增强版边生成算法（改进版）------------------------- def generate_realistic_edges_improved(node_features, avg_edge_count=20, atomic_nums=None, force_min_edges=True): if node_features is None or node_features.numel() == 0: return torch.empty((2, 0), dtype=torch.long), torch.tensor([]) num_nodes = node_features.shape[0] if num_nodes < 2: return torch.empty((2, 0), dtype=torch.long), torch.tensor([]) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] max_valence = {1: 1, 6: 4, 7: 3, 8: 2, 16: 6, 9: 1, 17: 1} # 计算节点相似度并添加额外的连接倾向 similarity = torch.matmul(node_features, node_features.transpose(0, 1)).squeeze() # 添加额外的连接偏向，确保分子整体性（避免孤立节点） connectivity_bias = torch.ones_like(similarity) - torch.eye(num_nodes, dtype=torch.long) similarity = similarity + 0.5 * connectivity_bias.float() norm_similarity = similarity / (torch.max(similarity) + 1e-8) edge_probs = norm_similarity.view(-1) num_possible_edges = num_nodes * (num_nodes - 1) num_edges = min(avg_edge_count, num_possible_edges) _, indices = torch.topk(edge_probs, k=num_edges) edge_set = set() edge_index = [] used_valence = {i: 0 for i in range(num_nodes)} bond_types = [] tried_edges = set() # 确保至少有一个连接，并优先连接苯环部分 if num_nodes >= 2 and force_min_edges: if num_nodes >= 6 and all(atomic_nums[i] == 6 for i in range(6)): # 先连接苯环形成基础结构 for i in range(6): j = (i + 1) % 6 if (i, j) not in tried_edges: edge_set.add((i, j)) edge_set.add((j, i)) edge_index.append([i, j]) edge_index.append([j, i]) used_valence[i] += 1 used_valence[j] += 1 bond_types.append(2 if i % 2 == 0 else 1) bond_types.append(2 if i % 2 == 0 else 1) tried_edges.add((i, j)) tried_edges.add((j, i)) # 连接苯环到第一个非苯环原子（确保分子整体性） if num_nodes > 6: first_non_benzene = 6 # 寻找与苯环连接最紧密的非苯环原子 max_sim = -1 best_benzene = 0 for i in range(6): sim = norm_similarity[i, first_non_benzene] if sim > max_sim: max_sim = sim best_benzene = i if (best_benzene, first_non_benzene) not in tried_edges: edge_set.add((best_benzene, first_non_benzene)) edge_set.add((first_non_benzene, best_benzene)) edge_index.append([best_benzene, first_non_benzene]) edge_index.append([first_non_benzene, best_benzene]) used_valence[best_benzene] += 1 used_valence[first_non_benzene] += 1 bond_types.append(1) bond_types.append(1) tried_edges.add((best_benzene, first_non_benzene)) tried_edges.add((first_non_benzene, best_benzene)) else: # 常规分子的最小连接（确保连通性） max_sim = -1 best_u, best_v = 0, 1 for i in range(num_nodes): for j in range(i + 1, num_nodes): if norm_similarity[i, j] > max_sim and (i, j) not in tried_edges: max_sim = norm_similarity[i, j] best_u, best_v = i, j edge_set.add((best_u, best_v)) edge_set.add((best_v, best_u)) edge_index.append([best_u, best_v]) edge_index.append([best_v, best_u]) used_valence[best_u] += 1 used_valence[best_v] += 1 bond_types.append(1) bond_types.append(1) tried_edges.add((best_u, best_v)) tried_edges.add((best_v, best_u)) # 改进边生成逻辑，确保分子连接性（优先连接未连通部分） for idx in indices: u, v = (idx // num_nodes).item(), (idx % num_nodes).item() if u == v or (u, v) in tried_edges or (v, u) in tried_edges: continue tried_edges.add((u, v)) tried_edges.add((v, u)) atom_u, atom_v = atomic_nums[u], atomic_nums[v] max_u, max_v = max_valence[atom_u], max_valence[atom_v] avail_u, avail_v = max_u - used_valence[u], max_v - used_valence[v] # 不允许两个氢原子相连 if atom_u == 1 and atom_v == 1: continue # 确保连接不会导致分子分裂（至少需要n-1条边连接n个节点） if len(edge_set) < num_nodes - 1: edge_set.add((u, v)) edge_set.add((v, u)) edge_index.append([u, v]) edge_index.append([v, u]) used_valence[u] += 1 used_valence[v] += 1 bond_types.append(1) bond_types.append(1) continue # 常规连接逻辑 if avail_u >= 1 and avail_v >= 1: edge_set.add((u, v)) edge_set.add((v, u)) edge_index.append([u, v]) edge_index.append([v, u]) used_valence[u] += 1 used_valence[v] += 1 bond_types.append(1) bond_types.append(1) continue # 尝试形成双键（增加分子稳定性） if atom_u != 1 and atom_v != 1 and avail_u >= 2 and avail_v >= 2 and random.random() < 0.3: edge_set.add((u, v)) edge_set.add((v, u)) edge_index.append([u, v]) edge_index.append([v, u]) used_valence[u] += 2 used_valence[v] += 2 bond_types.append(2) bond_types.append(2) # 确保分子连接性的最后检查 if len(edge_index) == 0 and num_nodes >= 2: edge_index = [[0, 1], [1, 0]] bond_types = [1, 1] used_valence[0], used_valence[1] = 1, 1 return torch.tensor(edge_index).t().contiguous(), torch.tensor(bond_types) @lru_cache(maxsize=128) def cached_calculate_tpsa(mol): try: return rdMolDescriptors.CalcTPSA(mol) if mol else 0 except: return 0 @lru_cache(maxsize=128) def cached_calculate_ring_penalty(mol): try: if mol: ssr = Chem.GetSymmSSSR(mol) return len(ssr) * 0.2 return 0 except: return 0 # ------------------------- Gumbel-Softmax 实现 ------------------------- def gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1): gumbels = -(torch.empty_like(logits).exponential_() + eps).log() gumbels = (logits + gumbels) / tau y_soft = gumbels.softmax(dim) if hard: index = y_soft.max(dim, keepdim=True)[1] y_hard = torch.zeros_like(logits).scatter_(dim, index, 1.0) ret = y_hard - y_soft.detach() + y_soft else: ret = y_soft return ret # ------------------------- 核心优化：强化化学约束（改进版）------------------------- def build_valid_mol_improved(atomic_nums, edge_index, bond_types=None): atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] bond_type_map = {1: Chem.BondType.SINGLE, 2: Chem.BondType.DOUBLE} mol = Chem.RWMol() atom_indices = [] for num in atomic_nums: atom = Chem.Atom(num) idx = mol.AddAtom(atom) atom_indices.append(idx) added_edges = set() if bond_types is not None: if isinstance(bond_types, torch.Tensor): bond_types = bond_types.cpu().numpy().tolist() if bond_types.is_cuda else bond_types.numpy().tolist() else: bond_types = [] max_valence = {1: 1, 6: 4, 7: 3, 8: 2, 16: 6, 9: 1, 17: 1} current_valence = {i: 0 for i in range(len(atomic_nums))} if edge_index.numel() > 0: # 按重要性排序边，优先连接形成单一片段（苯环连接优先） edge_importance = [] for j in range(edge_index.shape[1]): u, v = edge_index[0, j].item(), edge_index[1, j].item() # 计算边的重要性：苯环与非苯环连接 > 苯环内部连接 > 其他连接 importance = 2.0 if (u < 6 and v >= 6) or (v < 6 and u >= 6) else 1.5 if (u < 6 and v < 6) else 1.0 edge_importance.append((importance, j)) # 按重要性降序排序 edge_order = [j for _, j in sorted(edge_importance, key=lambda x: x[0], reverse=True)] for j in edge_order: u, v = edge_index[0, j].item(), edge_index[1, j].item() if u >= len(atomic_nums) or v >= len(atomic_nums) or u == v or (u, v) in added_edges: continue atom_u = mol.GetAtomWithIdx(atom_indices[u]) atom_v = mol.GetAtomWithIdx(atom_indices[v]) max_u = max_valence[atomic_nums[u]] max_v = max_valence[atomic_nums[v]] used_u = current_valence[u] used_v = current_valence[v] remain_u = max_u - used_u remain_v = max_v - used_v if remain_u >= 1 and remain_v >= 1: bond_type = bond_type_map.get(bond_types[j] if j < len(bond_types) else 1, Chem.BondType.SINGLE) bond_order = 1 if bond_type == Chem.BondType.SINGLE else 2 if bond_order > min(remain_u, remain_v): bond_order = 1 bond_type = Chem.BondType.SINGLE mol.AddBond(atom_indices[u], atom_indices[v], bond_type) added_edges.add((u, v)) added_edges.add((v, u)) current_valence[u] += bond_order current_valence[v] += bond_order # 更稳健的价态修复逻辑（优先保持分子连接性） try: Chem.SanitizeMol(mol) return mol except: # 先尝试添加氢原子修复（保持分子完整性） try: mol = Chem.AddHs(mol) Chem.SanitizeMol(mol) return mol except: pass # 智能移除键（仅移除导致价态问题的非关键键） try: problematic_bonds = [] for bond in mol.GetBonds(): begin = bond.GetBeginAtom() end = bond.GetEndAtom() if begin.GetExplicitValence() > max_valence[begin.GetAtomicNum()] or \ end.GetExplicitValence() > max_valence[end.GetAtomicNum()]: problematic_bonds.append(bond.GetIdx()) # 按键类型和连接重要性排序（先移除双键，再移除非苯环连接） problematic_bonds.sort(key=lambda idx: ( mol.GetBondWithIdx(idx).GetBondTypeAsDouble(), -1 if (mol.GetBondWithIdx(idx).GetBeginAtomIdx() < 6 and mol.GetBondWithIdx(idx).GetEndAtomIdx() >= 6) else 1 ), reverse=True) for bond_idx in problematic_bonds: mol.RemoveBond(mol.GetBondWithIdx(bond_idx).GetBeginAtomIdx(), mol.GetBondWithIdx(bond_idx).GetEndAtomIdx()) try: Chem.SanitizeMol(mol) break except: continue Chem.SanitizeMol(mol) return mol except: # 最后尝试移除氢原子（作为最后的修复手段） try: mol = Chem.RemoveHs(mol) Chem.SanitizeMol(mol) return mol except: return None def mol_to_graph(mol): if not mol: return None try: Chem.SanitizeMol(mol) atom_feats = [atom.GetAtomicNum() for atom in mol.GetAtoms()] edge_index = [] for bond in mol.GetBonds(): i, j = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() edge_index += [(i, j), (j, i)] x = torch.tensor(atom_feats, dtype=torch.long).view(-1, 1) edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous() return Data(x=x, edge_index=edge_index) except: return None def filter_valid_mols(mols): valid_mols = [] for mol in mols: if mol is None: continue try: Chem.SanitizeMol(mol) rdMolDescriptors.CalcExactMolWt(mol) valid_mols.append(mol) except: continue return valid_mols # ------------------------- 新增：分子片段过滤工具（改进版）------------------------- def filter_single_fragment_mols_improved(mol_list): valid_mols = [] for mol in mol_list: if mol is None: continue try: # 先尝试添加氢原子以确保分子完整性 mol = Chem.AddHs(mol) Chem.SanitizeMol(mol) # 使用更鲁棒的方法检测片段数 frags = Chem.GetMolFrags(mol, asMols=True, sanitizeFrags=False) if len(frags) == 1: # 进一步检查分子重量和原子数（过滤过小分子） mol_weight = rdMolDescriptors.CalcExactMolWt(mol) if mol_weight > 50 and mol.GetNumAtoms() > 5: valid_mols.append(mol) except: continue return valid_mols # ------------------------- 新增：苯环检测工具 ------------------------- def has_benzene_ring(mol): if mol is None: return False try: benzene_smarts = Chem.MolFromSmarts("c1ccccc1") return mol.HasSubstructMatch(benzene_smarts) except Exception as e: print(f"检测苯环失败: {e}") return False def filter_benzene_mols(mols): return [mol for mol in mols if has_benzene_ring(mol)] # ------------------------- 数据集 ------------------------- class MolecularGraphDataset(torch.utils.data.Dataset): def init(self, path): df = pd.read_excel(path, usecols=[0, 1, 2]) df.columns = ['SMILES', 'Concentration', 'Efficiency'] df.dropna(inplace=True) df['SMILES'] = df['SMILES'].apply(augment_mol) self.graphs, self.conditions = [], [] scaler = StandardScaler() cond_scaled = scaler.fit_transform(df[['Concentration', 'Efficiency']]) self.edge_stats = {'edge_counts': []} valid_count = 0 for i, row in df.iterrows(): graph = smiles_to_graph(row['SMILES']) if graph: atomic_nums = graph.x.squeeze().tolist() atomic_nums = validate_atomic_nums(atomic_nums) graph.x = torch.tensor(atomic_nums, dtype=torch.long).view(-1, 1) self.graphs.append(graph) self.conditions.append(torch.tensor(cond_scaled[i], dtype=torch.float32)) self.edge_stats['edge_counts'].append(graph.edge_index.shape[1] // 2) valid_count += 1 print(f"成功加载 {valid_count} 个分子") self.avg_edge_count = int(np.mean(self.edge_stats['edge_counts'])) if self.edge_stats['edge_counts'] else 20 print(f"真实图平均边数: {self.avg_edge_count}") def len(self): return len(self.graphs) def getitem(self, idx): return self.graphs[idx], self.conditions[idx] # ------------------------- 增强版生成器（含苯环条件编码） ------------------------- class EnhancedGraphGenerator(nn.Module): def init(self, noise_dim=16, condition_dim=2, benzene_condition_dim=1, hidden_dim=128, num_atoms=15): super().init() self.num_atoms = num_atoms self.benzene_embedding = nn.Embedding(2, hidden_dim) # 苯环条件嵌入 # 噪声和条件处理 self.initial_transform = nn.Sequential( nn.Linear(noise_dim + condition_dim + hidden_dim, hidden_dim), nn.LeakyReLU(0.2), nn.BatchNorm1d(hidden_dim), nn.Linear(hidden_dim, hidden_dim) ) # 苯环专用生成路径 self.benzene_generator = nn.Sequential( nn.Linear(hidden_dim, hidden_dim), nn.LeakyReLU(0.2), nn.Linear(hidden_dim, 6 * 7) # 6个碳原子，7种可能的原子类型 ) # 分子其余部分生成路径 self.rest_generator = nn.Sequential( nn.Linear(hidden_dim, hidden_dim), nn.LeakyReLU(0.2), nn.Linear(hidden_dim, (num_atoms - 6) * 7) # 剩余原子 ) # 原子间连接预测 self.edge_predictor = nn.Sequential( nn.Linear(hidden_dim, hidden_dim), nn.LeakyReLU(0.2), nn.Linear(hidden_dim, num_atoms * num_atoms) # 原子间连接概率 ) self.tau = 0.8 self.valid_atoms = torch.tensor([1, 6, 7, 8, 16, 9, 17], dtype=torch.long) def forward(self, z, cond, benzene_condition): # 嵌入苯环条件 benzene_feat = self.benzene_embedding(benzene_condition) # 合并噪声和条件 x = torch.cat([z, cond, benzene_feat], dim=1) shared_repr = self.initial_transform(x) # 生成苯环部分（前6个原子） benzene_logits = self.benzene_generator(shared_repr).view(-1, 6, 7) benzene_probs = gumbel_softmax(benzene_logits, tau=self.tau, hard=False, dim=-1) # 强制前6个原子为碳原子（苯环） benzene_indices = torch.ones_like(benzene_probs.argmax(dim=-1)) * 1 # 碳的索引是1 benzene_nodes = self.valid_atoms[benzene_indices].float().view(-1, 6, 1) / 17.0 # 生成分子其余部分 if self.num_atoms > 6: rest_logits = self.rest_generator(shared_repr).view(-1, self.num_atoms - 6, 7) rest_probs = gumbel_softmax(rest_logits, tau=self.tau, hard=False, dim=-1) rest_indices = rest_probs.argmax(dim=-1) rest_nodes = self.valid_atoms[rest_indices].float().view(-1, self.num_atoms - 6, 1) / 17.0 # 合并苯环和其余部分 node_feats = torch.cat([benzene_nodes, rest_nodes], dim=1) else: node_feats = benzene_nodes return node_feats # ------------------------- 增强版判别器 ------------------------- class EnhancedGraphDiscriminator(nn.Module): def init(self, node_feat_dim=1, condition_dim=2): super().init() # 图卷积层 self.conv1 = GCNConv(node_feat_dim + 1, 64) self.bn1 = nn.BatchNorm1d(64) self.conv2 = GATConv(64, 32, heads=3, concat=False) self.bn2 = nn.BatchNorm1d(32) self.conv3 = GraphConv(32, 16) self.bn3 = nn.BatchNorm1d(16) # 注意力机制 self.attention = nn.Sequential( nn.Linear(16, 8), nn.Tanh(), nn.Linear(8, 1) ) # 条件处理 self.condition_processor = nn.Sequential( nn.Linear(condition_dim, 16), nn.LeakyReLU(0.2) ) # 分类器 self.classifier = nn.Sequential( nn.Linear(16 + 16, 16), nn.LeakyReLU(0.2), nn.Linear(16, 1) ) def forward(self, data, condition): x, edge_index, batch = data.x.float(), data.edge_index, data.batch # 提取键类型特征 bond_features = torch.zeros(x.size(0), 1).to(x.device) if hasattr(data, 'bond_types') and data.bond_types is not None: bond_types = data.bond_types for i in range(edge_index.size(1)): u, v = edge_index[0, i].item(), edge_index[1, i].item() bond_type = bond_types[i] if i < len(bond_types) else 1 bond_features[u] = max(bond_features[u], bond_type) bond_features[v] = max(bond_features[v], bond_type) # 合并原子特征和键特征 x = torch.cat([x, bond_features], dim=1) # 图卷积 x = self.conv1(x, edge_index) x = self.bn1(x).relu() x = self.conv2(x, edge_index) x = self.bn2(x).relu() x = self.conv3(x, edge_index) x = self.bn3(x).relu() # 注意力池化 attn_weights = self.attention(x).squeeze() attn_weights = torch.softmax(attn_weights, dim=0) x = global_mean_pool(x * attn_weights.unsqueeze(-1), batch) # 处理条件 cond_repr = self.condition_processor(condition) # 合并图表示和条件 x = torch.cat([x, cond_repr], dim=1) # 分类 return torch.sigmoid(self.classifier(x)) # ------------------------- 训练流程（含苯环条件） ------------------------- def train_gan(dataset_path, epochs=2000, batch_size=32, lr=1e-5, device=None, generator_class=EnhancedGraphGenerator): if device is None: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") dataset = MolecularGraphDataset(dataset_path) if len(dataset) == 0: print("错误：数据集为空") return None, None def collate_fn(data_list): graphs, conditions = zip(data_list) batch_graphs = Batch.from_data_list(graphs) batch_conditions = torch.stack(conditions) return batch_graphs, batch_conditions dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn) # 创建生成器和判别器 generator = generator_class(noise_dim=16, condition_dim=2).to(device) discriminator = EnhancedGraphDiscriminator().to(device) # 调整学习率比例，让生成器学习更快 g_opt = optim.Adam(generator.parameters(), lr=lr 3, betas=(0.5, 0.999)) d_opt = optim.Adam(discriminator.parameters(), lr=lr, betas=(0.5, 0.999)) g_scheduler = optim.lr_scheduler.ReduceLROnPlateau(g_opt, 'min', patience=50, factor=0.5) d_scheduler = optim.lr_scheduler.ReduceLROnPlateau(d_opt, 'min', patience=50, factor=0.5) loss_fn = nn.BCELoss() use_amp = device.type == 'cuda' scaler = amp.GradScaler(enabled=use_amp) d_loss_history, g_loss_history = [], [] valid_mol_counts = [] fragment_counts = [] benzene_ratios = [] # 新增：记录含苯环分子比例 os.makedirs("results", exist_ok=True) os.chdir("results") for epoch in range(1, epochs + 1): generator.train() discriminator.train() total_d_loss, total_g_loss = 0.0, 0.0 for real_batch, conds in dataloader: real_batch = real_batch.to(device) conds = conds.to(device) batch_size = real_batch.num_graphs real_labels = torch.ones(batch_size, 1).to(device) fake_labels = torch.zeros(batch_size, 1).to(device) # 判别器训练 with amp.autocast(enabled=use_amp): d_real = discriminator(real_batch, conds) loss_real = loss_fn(d_real, real_labels) noise = torch.randn(batch_size, 16).to(device) # 生成苯环条件（50%概率要求含苯环） benzene_condition = torch.randint(0, 2, (batch_size,), device=device) fake_nodes = generator(noise, conds, benzene_condition).detach() fake_mols = [] for i in range(fake_nodes.shape[0]): node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] edge_index, bond_types = generate_realistic_edges_improved( node_feats, dataset.avg_edge_count, atomic_nums ) mol = build_valid_mol_improved(atomic_nums, edge_index, bond_types) if mol and mol.GetNumAtoms() > 0 and mol.GetNumBonds() > 0: fake_mols.append(mol_to_graph(mol)) if fake_mols: fake_batch = Batch.from_data_list(fake_mols).to(device) conds_subset = conds[:len(fake_mols)] d_fake = discriminator(fake_batch, conds_subset) loss_fake = loss_fn(d_fake, fake_labels[:len(fake_mols)]) d_loss = loss_real + loss_fake else: # 如果没有生成有效的分子，创建一个简单的分子作为占位符 mol = Chem.MolFromSmiles("CCO") fake_graph = mol_to_graph(mol) fake_batch = Batch.from_data_list([fake_graph]).to(device) d_fake = discriminator(fake_batch, conds[:1]) loss_fake = loss_fn(d_fake, fake_labels[:1]) d_loss = loss_real + loss_fake d_opt.zero_grad() scaler.scale(d_loss).backward() torch.nn.utils.clip_grad_norm_(discriminator.parameters(), 1.0) scaler.step(d_opt) scaler.update() total_d_loss += d_loss.item() # 生成器训练 with amp.autocast(enabled=use_amp): noise = torch.randn(batch_size, 16).to(device) benzene_condition = torch.randint(0, 2, (batch_size,), device=device) fake_nodes = generator(noise, conds, benzene_condition) fake_graphs = [] for i in range(fake_nodes.shape[0]): node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] edge_index, bond_types = generate_realistic_edges_improved( node_feats, dataset.avg_edge_count, atomic_nums ) fake_graphs.append(Data(x=node_feats, edge_index=edge_index, bond_types=bond_types)) valid_fake_graphs = [] for graph in fake_graphs: if graph.edge_index.numel() == 0: graph.edge_index = torch.tensor([[0, 0]], dtype=torch.long).t().to(device) graph.bond_types = torch.tensor([1], dtype=torch.long).to(device) valid_fake_graphs.append(graph) fake_batch = Batch.from_data_list(valid_fake_graphs).to(device) g_loss = loss_fn(discriminator(fake_batch, conds), real_labels) g_opt.zero_grad() scaler.scale(g_loss).backward() torch.nn.utils.clip_grad_norm_(generator.parameters(), 1.0) scaler.step(g_opt) scaler.update() total_g_loss += g_loss.item() avg_d_loss = total_d_loss / len(dataloader) avg_g_loss = total_g_loss / len(dataloader) d_loss_history.append(avg_d_loss) g_loss_history.append(avg_g_loss) g_scheduler.step(avg_g_loss) d_scheduler.step(avg_d_loss) if epoch % 10 == 0: generator.eval() discriminator.eval() with torch.no_grad(): num_samples = 50 noise = torch.randn(num_samples, 16).to(device) conds = torch.randn(num_samples, 2).to(device) benzene_condition = torch.ones(num_samples, dtype=torch.long, device=device) # 强制生成含苯环 fake_nodes = generator(noise, conds, benzene_condition) d_real_scores, d_fake_scores = [], [] for i in range(min(num_samples, 10)): real_idx = np.random.randint(0, len(dataset)) real_graph, real_cond = dataset[real_idx] real_batch = Batch.from_data_list([real_graph]).to(device) real_cond = real_cond.unsqueeze(0).to(device) d_real = discriminator(real_batch, real_cond) d_real_scores.append(d_real.item()) node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] edge_index, bond_types = generate_realistic_edges_improved( node_feats, dataset.avg_edge_count, atomic_nums ) mol = build_valid_mol_improved(atomic_nums, edge_index, bond_types) if mol: fake_graph = mol_to_graph(mol) if fake_graph: fake_batch = Batch.from_data_list([fake_graph]).to(device) fake_cond = conds[i].unsqueeze(0) d_fake = discriminator(fake_batch, fake_cond) d_fake_scores.append(d_fake.item()) if d_real_scores and d_fake_scores: print(f"Epoch {epoch}: D_loss={avg_d_loss:.4f}, G_loss={avg_g_loss:.4f}") print(f"D_real评分: {np.mean(d_real_scores):.4f} ± {np.std(d_real_scores):.4f}") print(f"D_fake评分: {np.mean(d_fake_scores):.4f} ± {np.std(d_fake_scores):.4f}") print(f"学习率: G={g_opt.param_groups[0]['lr']:.8f}, D={d_opt.param_groups[0]['lr']:.8f}") else: print(f"Epoch {epoch}: D_loss={avg_d_loss:.4f}, G_loss={avg_g_loss:.4f}") generator.train() if epoch % 100 == 0: torch.save(generator.state_dict(), f"generator_epoch_{epoch}.pt") torch.save(discriminator.state_dict(), f"discriminator_epoch_{epoch}.pt") generator.eval() with torch.no_grad(): num_samples = 25 noise = torch.randn(num_samples, 16).to(device) conds = torch.randn(num_samples, 2).to(device) benzene_condition = torch.ones(num_samples, dtype=torch.long, device=device) # 强制含苯环 fake_nodes = generator(noise, conds, benzene_condition) generated_mols = [] for i in range(num_samples): node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] edge_index, bond_types = generate_realistic_edges_improved( node_feats, dataset.avg_edge_count, atomic_nums ) mol = build_valid_mol_improved(atomic_nums, edge_index, bond_types) if mol and mol.GetNumAtoms() > 0 and mol.GetNumBonds() > 0: try: mol_weight = rdMolDescriptors.CalcExactMolWt(mol) logp = rdMolDescriptors.CalcCrippenDescriptors(mol)[0] tpsa = cached_calculate_tpsa(mol) generated_mols.append((mol, mol_weight, logp, tpsa)) except Exception as e: print(f"计算分子描述符时出错: {e}") # 过滤无效分子 valid_mols = filter_valid_mols([mol for mol, _, _, _ in generated_mols]) # 统计含苯环分子比例 benzene_mols = filter_benzene_mols(valid_mols) benzene_ratio = len(benzene_mols) / len(valid_mols) if valid_mols else 0 benzene_ratios.append(benzene_ratio) # 统计分子片段情况（使用改进的过滤函数） single_fragment_mols = filter_single_fragment_mols_improved(valid_mols) fragment_ratio = len(single_fragment_mols) / len(valid_mols) if valid_mols else 0 fragment_counts.append(fragment_ratio) valid_mol_counts.append(len(single_fragment_mols)) print(f"Epoch {epoch}: 生成{len(generated_mols)}个分子，初步过滤后保留{len(valid_mols)}个合法分子") print(f"Epoch {epoch}: 含苯环分子比例: {len(benzene_mols)}/{len(valid_mols)} = {benzene_ratio:.2%}") print( f"Epoch {epoch}: 单一片段分子比例: {len(single_fragment_mols)}/{len(valid_mols)} = {fragment_ratio:.2%}") if single_fragment_mols: filtered_mols = [] for mol in single_fragment_mols: try: mol_weight = rdMolDescriptors.CalcExactMolWt(mol) logp = rdMolDescriptors.CalcCrippenDescriptors(mol)[0] tpsa = cached_calculate_tpsa(mol) filtered_mols.append((mol, mol_weight, logp, tpsa)) except: continue if filtered_mols: filtered_mols.sort(key=lambda x: x[1]) mols = [mol for mol, _, _, _ in filtered_mols] legends = [ f"Mol {i + 1}\nMW: {mw:.2f}\nLogP: {logp:.2f}\nTPSA: {tpsa:.2f}" for i, (_, mw, logp, tpsa) in enumerate(filtered_mols) ] img = Draw.MolsToGridImage(mols, molsPerRow=5, subImgSize=(200, 200), legends=legends) img.save(f"generated_molecules_epoch_{epoch}.png") print(f"Epoch {epoch}: 保存{len(filtered_mols)}个单一片段分子的可视化结果") else: print(f"Epoch {epoch}: 过滤后无合法单一片段分子可显示") else: print(f"Epoch {epoch}: 未生成任何合法单一片段分子") generator.train() # 绘制损失曲线 plt.figure(figsize=(10, 5)) plt.plot(d_loss_history, label='Discriminator Loss') plt.plot(g_loss_history, label='Generator Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.title('GAN Loss Curve') plt.legend() plt.savefig('gan_loss_curve.png') plt.close() # 绘制合法分子数量曲线 plt.figure(figsize=(10, 5)) plt.plot([i * 100 for i in range(1, len(valid_mol_counts) + 1)], valid_mol_counts, label='Valid Single-Fragment Molecules') plt.xlabel('Epoch') plt.ylabel('Number of Valid Molecules') plt.title('Number of Valid Single-Fragment Molecules Generated per Epoch') plt.legend() plt.savefig('valid_molecules_curve.png') plt.close() # 绘制分子片段比例曲线 plt.figure(figsize=(10, 5)) plt.plot([i * 100 for i in range(1, len(fragment_counts) + 1)], fragment_counts, label='Single-Fragment Ratio') plt.xlabel('Epoch') plt.ylabel('Single-Fragment Molecules Ratio') plt.title('Ratio of Single-Fragment Molecules in Generated Molecules') plt.legend() plt.savefig('fragment_ratio_curve.png') plt.close() # 绘制苯环比例曲线 plt.figure(figsize=(10, 5)) plt.plot([i * 100 for i in range(1, len(benzene_ratios) + 1)], benzene_ratios, label='Benzene Ring Ratio') plt.xlabel('Epoch') plt.ylabel('Benzene Ring Molecules Ratio') plt.title('Ratio of Molecules Containing Benzene Rings') plt.legend() plt.savefig('benzene_ratio_curve.png') plt.close() return generator, discriminator # ------------------------- 参考分子处理与批量生成（改进版）------------------------- def process_reference_smiles(reference_smiles): ref_graph = smiles_to_graph(reference_smiles) if ref_graph is None: raise ValueError("参考分子SMILES转换图结构失败，请检查SMILES合法性") ref_concentration = 1.0 ref_efficiency = 0.8 ref_condition = torch.tensor([ref_concentration, ref_efficiency], dtype=torch.float32) return ref_graph, ref_condition def generate_molecules_with_reference_improved(generator, reference_smiles, num_samples=1000, noise_type="gaussian", force_benzene=True, device=None): if device is None: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ref_graph, ref_condition = process_reference_smiles(reference_smiles) ref_condition_batch = ref_condition.unsqueeze(0).repeat(num_samples, 1).to(device) if noise_type.lower() == "gaussian": noise = torch.randn(num_samples, 16).to(device) elif noise_type.lower() == "uniform": noise = 2 * torch.rand(num_samples, 16).to(device) - 1 else: raise ValueError("噪声类型必须是 'gaussian' 或 'uniform'") generator.eval() generated_mols = [] # 强制生成含苯环分子 benzene_condition = torch.ones(num_samples, dtype=torch.long, device=device) if force_benzene else \ torch.randint(0, 2, (num_samples,), device=device) with torch.no_grad(): fake_nodes = generator(noise, ref_condition_batch, benzene_condition) for i in range(num_samples): node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] # 使用改进的边生成函数 edge_index, bond_types = generate_realistic_edges_improved( node_feats, 20, atomic_nums ) # 使用改进的分子构建函数 mol = build_valid_mol_improved(atomic_nums, edge_index, bond_types) if mol and mol.GetNumAtoms() > 0 and mol.GetNumBonds() > 0: generated_mols.append(mol) # 过滤无效分子 valid_mols = filter_valid_mols(generated_mols) print(f"使用{noise_type}噪声，基于参考分子生成 {num_samples} 个分子，初步过滤后有效分子: {len(valid_mols)}") # 过滤含苯环的分子（如果强制要求） if force_benzene: benzene_mols = filter_benzene_mols(valid_mols) print(f"进一步过滤后，含苯环分子数量: {len(benzene_mols)}") print(f"含苯环分子比例: {len(benzene_mols) / len(valid_mols):.2%}") valid_mols = benzene_mols # 使用改进的单一片段过滤函数 single_fragment_mols = filter_single_fragment_mols_improved(valid_mols) print(f"进一步过滤后，单一片段分子数量: {len(single_fragment_mols)}") # 计算片段比例 fragment_ratio = len(single_fragment_mols) / len(valid_mols) if valid_mols else 0 print(f"单一片段分子比例: {fragment_ratio:.2%}") return single_fragment_mols # ------------------------- 分子属性计算与保存 ------------------------- def calculate_molecular_properties(mols): properties = [] for mol in mols: try: mol_weight = rdMolDescriptors.CalcExactMolWt(mol) logp = rdMolDescriptors.CalcCrippenDescriptors(mol)[0] tpsa = rdMolDescriptors.CalcTPSA(mol) hba = rdMolDescriptors.CalcNumHBA(mol) hbd = rdMolDescriptors.CalcNumHBD(mol) rot_bonds = rdMolDescriptors.CalcNumRotatableBonds(mol) n_count = sum(1 for atom in mol.GetAtoms() if atom.GetAtomicNum() == 7) o_count = sum(1 for atom in mol.GetAtoms() if atom.GetAtomicNum() == 8) s_count = sum(1 for atom in mol.GetAtoms() if atom.GetAtomicNum() == 16) p_count = sum(1 for atom in mol.GetAtoms() if atom.GetAtomicNum() == 15) frags = Chem.GetMolFrags(mol, asMols=True) fragment_count = len(frags) # 检测苯环 has_benzene = has_benzene_ring(mol) properties.append({ 'SMILES': Chem.MolToSmiles(mol), 'MW': mol_weight, 'LogP': logp, 'TPSA': tpsa, 'HBA': hba, 'HBD': hbd, 'RotBonds': rot_bonds, 'N_count': n_count, 'O_count': o_count, 'S_count': s_count, 'P_count': p_count, 'FragmentCount': fragment_count, 'HasBenzene': has_benzene }) except Exception as e: print(f"计算分子属性时出错: {e}") continue return properties def save_molecules(mols, prefix="generated", noise_type="gaussian"): if not mols: print("没有分子可保存") return subdir = f"{prefix}_{noise_type}" os.makedirs(subdir, exist_ok=True) if len(mols) <= 100: legends = [f"Mol {i + 1}" for i in range(len(mols))] img = Draw.MolsToGridImage(mols, molsPerRow=5, subImgSize=(300, 300), legends=legends) img.save(f"{subdir}/{prefix}_{noise_type}_molecules.png") properties = calculate_molecular_properties(mols) df = pd.DataFrame(properties) df.to_csv(f"{subdir}/{prefix}_{noise_type}_properties.csv", index=False) with open(f"{subdir}/{prefix}_{noise_type}_smiles.smi", "w") as f: for props in properties: f.write(f"{props['SMILES']}\n") print(f"已保存 {len(mols)} 个单一片段分子到目录: {subdir}") # ------------------------- 新增：生成结果分析工具 ------------------------- def analyze_generated_molecules(mols_gaussian, mols_uniform): print("\n===== 生成分子分析报告 =====") count_gaussian = len(mols_gaussian) count_uniform = len(mols_uniform) print(f"高斯噪声生成单一片段分子: {count_gaussian}") print(f"均匀噪声生成单一片段分子: {count_uniform}") def calculate_avg_properties(mols): if not mols: return {} props = calculate_molecular_properties(mols) avg_props = {} for key in props[0].keys(): if key != 'SMILES': avg_props[key] = sum(p[key] for p in props) / len(props) return avg_props avg_gaussian = calculate_avg_properties(mols_gaussian) avg_uniform = calculate_avg_properties(mols_uniform) if avg_gaussian and avg_uniform: print("\n高斯噪声生成分子的平均属性:") for key, value in avg_gaussian.items(): print(f" {key}: {value:.2f}") print("\n均匀噪声生成分子的平均属性:") for key, value in avg_uniform.items(): print(f" {key}: {value:.2f}") print("\n属性差异 (均匀 - 高斯):") for key in avg_gaussian.keys(): if key != 'SMILES': diff = avg_uniform[key] - avg_gaussian[key] print(f" {key}: {diff:+.2f}") if mols_gaussian and mols_uniform: properties = ['MW', 'LogP', 'TPSA', 'HBA', 'HBD', 'RotBonds'] plt.figure(figsize=(15, 10)) for i, prop in enumerate(properties, 1): plt.subplot(2, 3, i) gaussian_vals = [p[prop] for p in calculate_molecular_properties(mols_gaussian)] uniform_vals = [p[prop] for p in calculate_molecular_properties(mols_uniform)] plt.hist(gaussian_vals, bins=20, alpha=0.5, label='Gaussian') plt.hist(uniform_vals, bins=20, alpha=0.5, label='Uniform') plt.title(f'{prop} Distribution') plt.xlabel(prop) plt.ylabel('Frequency') plt.legend() plt.tight_layout() plt.savefig('molecular_property_distribution.png') plt.close() print("\n分子属性分布图已保存为 'molecular_property_distribution.png'") # ------------------------- 新增：分子可视化工具 ------------------------- def visualize_molecules_grid(molecules, num_per_row=5, filename="molecules_grid.png", legends=None): """创建高质量的分子网格可视化图""" if not molecules: print("没有分子可可视化") return if legends is None: legends = [f"Molecule {i + 1}" for i in range(len(molecules))] try: img = Draw.MolsToGridImage( molecules, molsPerRow=num_per_row, subImgSize=(300, 300), legends=legends, useSVG=False, highlightAtomLists=None, highlightBondLists=None ) img.save(filename) print(f"分子网格图已保存至: {filename}") return img except Exception as e: print(f"生成分子网格图时出错: {e}") return None # ------------------------- 主函数 ------------------------- def main(): print("=" * 80) print("基于合法SMILES的分子生成GAN系统（改进版）") print("=" * 80) dataset_path = "D:\python\pythonProject1\DATA\Inhibitor1368_data.xlsx" if not os.path.exists(dataset_path): print(f"错误：数据集文件 '{dataset_path}' 不存在！") exit(1) print(f"开始加载数据集: {dataset_path}") print("=" * 80) print("开始训练分子生成GAN...") generator, discriminator = train_gan( dataset_path=dataset_path, epochs=200, batch_size=32, lr=1e-5, ) print("=" * 80) # 设置参考缓蚀剂分子（确保是单一片段含苯环的有效分子） reference_smiles = "NCCNCc1ccc(O)c2ncccc12" if generator: print("训练完成！模型和生成结果已保存") print("生成的分子可视化结果在'results'目录") print("损失曲线已保存为'gan_loss_curve.png'") print("合法分子数量曲线已保存为'valid_molecules_curve.png'") print("分子片段比例曲线已保存为'fragment_ratio_curve.png'") print("苯环比例曲线已保存为'benzene_ratio_curve.png'") # 基于参考分子生成1000个新分子（高斯噪声，强制含苯环） print("\n开始基于参考分子生成新分子（高斯噪声，强制含苯环）...") gaussian_mols = generate_molecules_with_reference_improved( generator, reference_smiles, num_samples=1000, noise_type="gaussian", force_benzene=True # 强制生成含苯环分子 ) save_molecules(gaussian_mols, prefix="ref_based", noise_type="gaussian_benzene") # 可视化最佳分子 if gaussian_mols: # 计算每个分子的QED分数 qed_scores = [] for mol in gaussian_mols: try: qed_scores.append(rdMolDescriptors.CalcQED(mol)) except: qed_scores.append(0) # 按QED分数排序 sorted_indices = sorted(range(len(qed_scores)), key=lambda i: qed_scores[i], reverse=True) top_molecules = [gaussian_mols[i] for i in sorted_indices[:25]] top_legends = [f"QED: {qed_scores[i]:.3f}" for i in sorted_indices[:25]] # 可视化 visualize_molecules_grid( top_molecules, num_per_row=5, filename="top_molecules_by_qed.png", legends=top_legends ) print("已生成并保存最佳分子的可视化结果") # 基于参考分子生成1000个新分子（均匀噪声，强制含苯环） print("\n开始基于参考分子生成新分子（均匀噪声，强制含苯环）...") uniform_mols = generate_molecules_with_reference_improved( generator, reference_smiles, num_samples=1000, noise_type="uniform", force_benzene=True # 强制生成含苯环分子 ) save_molecules(uniform_mols, prefix="ref_based", noise_type="uniform_benzene") # 分析两种噪声生成的分子差异 if gaussian_mols and uniform_mols: analyze_generated_molecules(gaussian_mols, uniform_mols) else: print("训练过程中未生成合法分子，请检查数据") print("=" * 80) if name == "main": main()该代码生成的新分子符合要求的非常非常少如何改进

validity_reward = valid_count / batch_size # 0到1的比例 # 原始对抗损失 + 合法性奖励 g_loss = loss_fn(d_fake, real_labels) - 0.5 * torch.log(validity_reward + 1e-8) # 新增：周期性验证 if epoch...

使用gaussian噪声，基于参考分子生成 1000 个分子，初步过滤后有效分子: 1000 进一步过滤后，含苯环分子数量: 939 含苯环分子比例: 93.90% [15:31:55] WARNING: could not find number of expected rings. Switching to an approximate ring finding algorithm. 进一步过滤后，单一片段分子数量: 0 单一片段分子比例: 0.00% 没有分子可保存开始基于参考分子生成新分子（均匀噪声，强制含苯环）... [15:31:55] WARNING: could not find number of expected rings. Switching to an approximate ring finding algorithm. [15:31:55] WARNING: could not find number of expected rings. Switching to an approximate ring finding algorithm. [15:31:55] WARNING: could not find number of expected rings. Switching to an approximate ring finding algorithm. 使用uniform噪声，基于参考分子生成 1000 个分子，初步过滤后有效分子: 1000 [15:31:56] WARNING: could not find number of expected rings. Switching to an approximate ring finding algorithm. 进一步过滤后，含苯环分子数量: 957 含苯环分子比例: 95.70% [15:31:56] WARNING: could not find number of expected rings. Switching to an approximate ring finding algorithm. [15:31:56] WARNING: could not find number of expected rings. Switching to an approximate ring finding algorithm. 进一步过滤后，单一片段分子数量: 1 单一片段分子比例: 0.10%这个是下面的代码运行的结果import torch import torch.nn as nn import torch.optim as optim import pandas as pd from rdkit import Chem from rdkit.Chem import Draw, rdMolDescriptors from torch_geometric.data import Data, Batch from torch_geometric.nn import GCNConv, global_mean_pool, GATConv, GraphConv from torch_geometric.loader import DataLoader import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler import os import numpy as np import random from functools import lru_cache from torch.cuda import amp # 设置随机种子以确保结果可复现 def set_seed(seed): torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False set_seed(42) # ------------------------- 工具函数 ------------------------- def validate_atomic_nums(atomic_nums): valid_atoms = {1, 6, 7, 8, 16, 9, 17} # H, C, N, O, S, F, Cl if isinstance(atomic_nums, torch.Tensor): atomic_nums = atomic_nums.cpu().numpy() if atomic_nums.is_cuda else atomic_nums.numpy() if isinstance(atomic_nums, list): atomic_nums = np.array(atomic_nums) if atomic_nums.ndim > 1: atomic_nums = atomic_nums.flatten() atomic_nums = np.round(atomic_nums).astype(int) return [num if num in valid_atoms else 6 for num in atomic_nums] def smiles_to_graph(smiles): mol = Chem.MolFromSmiles(smiles) if mol is None: return None Chem.SanitizeMol(mol) atom_feats = [atom.GetAtomicNum() for atom in mol.GetAtoms()] edge_index = [] for bond in mol.GetBonds(): i, j = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() edge_index += [(i, j), (j, i)] x = torch.tensor(atom_feats, dtype=torch.long).view(-1, 1) edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous() return Data(x=x, edge_index=edge_index) def augment_mol(smiles): mol = Chem.MolFromSmiles(smiles) if mol: try: mol = Chem.AddHs(mol) except: pass return Chem.MolToSmiles(mol) return smiles # ------------------------- 增强版边生成算法（改进版）------------------------- def generate_realistic_edges_improved(node_features, avg_edge_count=20, atomic_nums=None, force_min_edges=True): if node_features is None or node_features.numel() == 0: return torch.empty((2, 0), dtype=torch.long), torch.tensor([]) num_nodes = node_features.shape[0] if num_nodes < 2: return torch.empty((2, 0), dtype=torch.long), torch.tensor([]) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] max_valence = {1: 1, 6: 4, 7: 3, 8: 2, 16: 6, 9: 1, 17: 1} # 计算节点相似度并添加额外的连接倾向 similarity = torch.matmul(node_features, node_features.transpose(0, 1)).squeeze() # 添加额外的连接偏向，确保分子整体性（避免孤立节点） connectivity_bias = torch.ones_like(similarity) - torch.eye(num_nodes, dtype=torch.long) similarity = similarity + 0.5 * connectivity_bias.float() norm_similarity = similarity / (torch.max(similarity) + 1e-8) edge_probs = norm_similarity.view(-1) num_possible_edges = num_nodes * (num_nodes - 1) num_edges = min(avg_edge_count, num_possible_edges) _, indices = torch.topk(edge_probs, k=num_edges) edge_set = set() edge_index = [] used_valence = {i: 0 for i in range(num_nodes)} bond_types = [] tried_edges = set() # 确保至少有一个连接，并优先连接苯环部分 if num_nodes >= 2 and force_min_edges: if num_nodes >= 6 and all(atomic_nums[i] == 6 for i in range(6)): # 先连接苯环形成基础结构 for i in range(6): j = (i + 1) % 6 if (i, j) not in tried_edges: edge_set.add((i, j)) edge_set.add((j, i)) edge_index.append([i, j]) edge_index.append([j, i]) used_valence[i] += 1 used_valence[j] += 1 bond_types.append(2 if i % 2 == 0 else 1) bond_types.append(2 if i % 2 == 0 else 1) tried_edges.add((i, j)) tried_edges.add((j, i)) # 连接苯环到第一个非苯环原子（确保分子整体性） if num_nodes > 6: first_non_benzene = 6 # 寻找与苯环连接最紧密的非苯环原子 max_sim = -1 best_benzene = 0 for i in range(6): sim = norm_similarity[i, first_non_benzene] if sim > max_sim: max_sim = sim best_benzene = i if (best_benzene, first_non_benzene) not in tried_edges: edge_set.add((best_benzene, first_non_benzene)) edge_set.add((first_non_benzene, best_benzene)) edge_index.append([best_benzene, first_non_benzene]) edge_index.append([first_non_benzene, best_benzene]) used_valence[best_benzene] += 1 used_valence[first_non_benzene] += 1 bond_types.append(1) bond_types.append(1) tried_edges.add((best_benzene, first_non_benzene)) tried_edges.add((first_non_benzene, best_benzene)) else: # 常规分子的最小连接（确保连通性） max_sim = -1 best_u, best_v = 0, 1 for i in range(num_nodes): for j in range(i + 1, num_nodes): if norm_similarity[i, j] > max_sim and (i, j) not in tried_edges: max_sim = norm_similarity[i, j] best_u, best_v = i, j edge_set.add((best_u, best_v)) edge_set.add((best_v, best_u)) edge_index.append([best_u, best_v]) edge_index.append([best_v, best_u]) used_valence[best_u] += 1 used_valence[best_v] += 1 bond_types.append(1) bond_types.append(1) tried_edges.add((best_u, best_v)) tried_edges.add((best_v, best_u)) # 改进边生成逻辑，确保分子连接性（优先连接未连通部分） for idx in indices: u, v = (idx // num_nodes).item(), (idx % num_nodes).item() if u == v or (u, v) in tried_edges or (v, u) in tried_edges: continue tried_edges.add((u, v)) tried_edges.add((v, u)) atom_u, atom_v = atomic_nums[u], atomic_nums[v] max_u, max_v = max_valence[atom_u], max_valence[atom_v] avail_u, avail_v = max_u - used_valence[u], max_v - used_valence[v] # 不允许两个氢原子相连 if atom_u == 1 and atom_v == 1: continue # 确保连接不会导致分子分裂（至少需要n-1条边连接n个节点） if len(edge_set) < num_nodes - 1: edge_set.add((u, v)) edge_set.add((v, u)) edge_index.append([u, v]) edge_index.append([v, u]) used_valence[u] += 1 used_valence[v] += 1 bond_types.append(1) bond_types.append(1) continue # 常规连接逻辑 if avail_u >= 1 and avail_v >= 1: edge_set.add((u, v)) edge_set.add((v, u)) edge_index.append([u, v]) edge_index.append([v, u]) used_valence[u] += 1 used_valence[v] += 1 bond_types.append(1) bond_types.append(1) continue # 尝试形成双键（增加分子稳定性） if atom_u != 1 and atom_v != 1 and avail_u >= 2 and avail_v >= 2 and random.random() < 0.3: edge_set.add((u, v)) edge_set.add((v, u)) edge_index.append([u, v]) edge_index.append([v, u]) used_valence[u] += 2 used_valence[v] += 2 bond_types.append(2) bond_types.append(2) # 确保分子连接性的最后检查 if len(edge_index) == 0 and num_nodes >= 2: edge_index = [[0, 1], [1, 0]] bond_types = [1, 1] used_valence[0], used_valence[1] = 1, 1 return torch.tensor(edge_index).t().contiguous(), torch.tensor(bond_types) @lru_cache(maxsize=128) def cached_calculate_tpsa(mol): try: return rdMolDescriptors.CalcTPSA(mol) if mol else 0 except: return 0 @lru_cache(maxsize=128) def cached_calculate_ring_penalty(mol): try: if mol: ssr = Chem.GetSymmSSSR(mol) return len(ssr) * 0.2 return 0 except: return 0 # ------------------------- Gumbel-Softmax 实现 ------------------------- def gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1): gumbels = -(torch.empty_like(logits).exponential_() + eps).log() gumbels = (logits + gumbels) / tau y_soft = gumbels.softmax(dim) if hard: index = y_soft.max(dim, keepdim=True)[1] y_hard = torch.zeros_like(logits).scatter_(dim, index, 1.0) ret = y_hard - y_soft.detach() + y_soft else: ret = y_soft return ret # ------------------------- 核心优化：强化化学约束（改进版）------------------------- def build_valid_mol_improved(atomic_nums, edge_index, bond_types=None): atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] bond_type_map = {1: Chem.BondType.SINGLE, 2: Chem.BondType.DOUBLE} mol = Chem.RWMol() atom_indices = [] for num in atomic_nums: atom = Chem.Atom(num) idx = mol.AddAtom(atom) atom_indices.append(idx) added_edges = set() if bond_types is not None: if isinstance(bond_types, torch.Tensor): bond_types = bond_types.cpu().numpy().tolist() if bond_types.is_cuda else bond_types.numpy().tolist() else: bond_types = [] max_valence = {1: 1, 6: 4, 7: 3, 8: 2, 16: 6, 9: 1, 17: 1} current_valence = {i: 0 for i in range(len(atomic_nums))} if edge_index.numel() > 0: # 按重要性排序边，优先连接形成单一片段（苯环连接优先） edge_importance = [] for j in range(edge_index.shape[1]): u, v = edge_index[0, j].item(), edge_index[1, j].item() # 计算边的重要性：苯环与非苯环连接 > 苯环内部连接 > 其他连接 importance = 2.0 if (u < 6 and v >= 6) or (v < 6 and u >= 6) else 1.5 if (u < 6 and v < 6) else 1.0 edge_importance.append((importance, j)) # 按重要性降序排序 edge_order = [j for _, j in sorted(edge_importance, key=lambda x: x[0], reverse=True)] for j in edge_order: u, v = edge_index[0, j].item(), edge_index[1, j].item() if u >= len(atomic_nums) or v >= len(atomic_nums) or u == v or (u, v) in added_edges: continue atom_u = mol.GetAtomWithIdx(atom_indices[u]) atom_v = mol.GetAtomWithIdx(atom_indices[v]) max_u = max_valence[atomic_nums[u]] max_v = max_valence[atomic_nums[v]] used_u = current_valence[u] used_v = current_valence[v] remain_u = max_u - used_u remain_v = max_v - used_v if remain_u >= 1 and remain_v >= 1: bond_type = bond_type_map.get(bond_types[j] if j < len(bond_types) else 1, Chem.BondType.SINGLE) bond_order = 1 if bond_type == Chem.BondType.SINGLE else 2 if bond_order > min(remain_u, remain_v): bond_order = 1 bond_type = Chem.BondType.SINGLE mol.AddBond(atom_indices[u], atom_indices[v], bond_type) added_edges.add((u, v)) added_edges.add((v, u)) current_valence[u] += bond_order current_valence[v] += bond_order # 更稳健的价态修复逻辑（优先保持分子连接性） try: Chem.SanitizeMol(mol) return mol except: # 先尝试添加氢原子修复（保持分子完整性） try: mol = Chem.AddHs(mol) Chem.SanitizeMol(mol) return mol except: pass # 智能移除键（仅移除导致价态问题的非关键键） try: problematic_bonds = [] for bond in mol.GetBonds(): begin = bond.GetBeginAtom() end = bond.GetEndAtom() if begin.GetExplicitValence() > max_valence[begin.GetAtomicNum()] or \ end.GetExplicitValence() > max_valence[end.GetAtomicNum()]: problematic_bonds.append(bond.GetIdx()) # 按键类型和连接重要性排序（先移除双键，再移除非苯环连接） problematic_bonds.sort(key=lambda idx: ( mol.GetBondWithIdx(idx).GetBondTypeAsDouble(), -1 if (mol.GetBondWithIdx(idx).GetBeginAtomIdx() < 6 and mol.GetBondWithIdx(idx).GetEndAtomIdx() >= 6) else 1 ), reverse=True) for bond_idx in problematic_bonds: mol.RemoveBond(mol.GetBondWithIdx(bond_idx).GetBeginAtomIdx(), mol.GetBondWithIdx(bond_idx).GetEndAtomIdx()) try: Chem.SanitizeMol(mol) break except: continue Chem.SanitizeMol(mol) return mol except: # 最后尝试移除氢原子（作为最后的修复手段） try: mol = Chem.RemoveHs(mol) Chem.SanitizeMol(mol) return mol except: return None def mol_to_graph(mol): if not mol: return None try: Chem.SanitizeMol(mol) atom_feats = [atom.GetAtomicNum() for atom in mol.GetAtoms()] edge_index = [] for bond in mol.GetBonds(): i, j = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() edge_index += [(i, j), (j, i)] x = torch.tensor(atom_feats, dtype=torch.long).view(-1, 1) edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous() return Data(x=x, edge_index=edge_index) except: return None def filter_valid_mols(mols): valid_mols = [] for mol in mols: if mol is None: continue try: Chem.SanitizeMol(mol) rdMolDescriptors.CalcExactMolWt(mol) valid_mols.append(mol) except: continue return valid_mols # ------------------------- 新增：分子片段过滤工具（改进版）------------------------- def filter_single_fragment_mols_improved(mol_list): valid_mols = [] for mol in mol_list: if mol is None: continue try: # 先尝试添加氢原子以确保分子完整性 mol = Chem.AddHs(mol) Chem.SanitizeMol(mol) # 使用更鲁棒的方法检测片段数 frags = Chem.GetMolFrags(mol, asMols=True, sanitizeFrags=False) if len(frags) == 1: # 进一步检查分子重量和原子数（过滤过小分子） mol_weight = rdMolDescriptors.CalcExactMolWt(mol) if mol_weight > 50 and mol.GetNumAtoms() > 5: valid_mols.append(mol) except: continue return valid_mols # ------------------------- 新增：苯环检测工具 ------------------------- def has_benzene_ring(mol): if mol is None: return False try: benzene_smarts = Chem.MolFromSmarts("c1ccccc1") return mol.HasSubstructMatch(benzene_smarts) except Exception as e: print(f"检测苯环失败: {e}") return False def filter_benzene_mols(mols): return [mol for mol in mols if has_benzene_ring(mol)] # ------------------------- 数据集 ------------------------- class MolecularGraphDataset(torch.utils.data.Dataset): def init(self, path): df = pd.read_excel(path, usecols=[0, 1, 2]) df.columns = ['SMILES', 'Concentration', 'Efficiency'] df.dropna(inplace=True) df['SMILES'] = df['SMILES'].apply(augment_mol) self.graphs, self.conditions = [], [] scaler = StandardScaler() cond_scaled = scaler.fit_transform(df[['Concentration', 'Efficiency']]) self.edge_stats = {'edge_counts': []} valid_count = 0 for i, row in df.iterrows(): graph = smiles_to_graph(row['SMILES']) if graph: atomic_nums = graph.x.squeeze().tolist() atomic_nums = validate_atomic_nums(atomic_nums) graph.x = torch.tensor(atomic_nums, dtype=torch.long).view(-1, 1) self.graphs.append(graph) self.conditions.append(torch.tensor(cond_scaled[i], dtype=torch.float32)) self.edge_stats['edge_counts'].append(graph.edge_index.shape[1] // 2) valid_count += 1 print(f"成功加载 {valid_count} 个分子") self.avg_edge_count = int(np.mean(self.edge_stats['edge_counts'])) if self.edge_stats['edge_counts'] else 20 print(f"真实图平均边数: {self.avg_edge_count}") def len(self): return len(self.graphs) def getitem(self, idx): return self.graphs[idx], self.conditions[idx] # ------------------------- 增强版生成器（含苯环条件编码） ------------------------- class EnhancedGraphGenerator(nn.Module): def init(self, noise_dim=16, condition_dim=2, benzene_condition_dim=1, hidden_dim=128, num_atoms=15): super().init() self.num_atoms = num_atoms self.benzene_embedding = nn.Embedding(2, hidden_dim) # 苯环条件嵌入 # 噪声和条件处理 self.initial_transform = nn.Sequential( nn.Linear(noise_dim + condition_dim + hidden_dim, hidden_dim), nn.LeakyReLU(0.2), nn.BatchNorm1d(hidden_dim), nn.Linear(hidden_dim, hidden_dim) ) # 苯环专用生成路径 self.benzene_generator = nn.Sequential( nn.Linear(hidden_dim, hidden_dim), nn.LeakyReLU(0.2), nn.Linear(hidden_dim, 6 * 7) # 6个碳原子，7种可能的原子类型 ) # 分子其余部分生成路径 self.rest_generator = nn.Sequential( nn.Linear(hidden_dim, hidden_dim), nn.LeakyReLU(0.2), nn.Linear(hidden_dim, (num_atoms - 6) * 7) # 剩余原子 ) # 原子间连接预测 self.edge_predictor = nn.Sequential( nn.Linear(hidden_dim, hidden_dim), nn.LeakyReLU(0.2), nn.Linear(hidden_dim, num_atoms * num_atoms) # 原子间连接概率 ) self.tau = 0.8 self.valid_atoms = torch.tensor([1, 6, 7, 8, 16, 9, 17], dtype=torch.long) def forward(self, z, cond, benzene_condition): # 嵌入苯环条件 benzene_feat = self.benzene_embedding(benzene_condition) # 合并噪声和条件 x = torch.cat([z, cond, benzene_feat], dim=1) shared_repr = self.initial_transform(x) # 生成苯环部分（前6个原子） benzene_logits = self.benzene_generator(shared_repr).view(-1, 6, 7) benzene_probs = gumbel_softmax(benzene_logits, tau=self.tau, hard=False, dim=-1) # 强制前6个原子为碳原子（苯环） benzene_indices = torch.ones_like(benzene_probs.argmax(dim=-1)) * 1 # 碳的索引是1 benzene_nodes = self.valid_atoms[benzene_indices].float().view(-1, 6, 1) / 17.0 # 生成分子其余部分 if self.num_atoms > 6: rest_logits = self.rest_generator(shared_repr).view(-1, self.num_atoms - 6, 7) rest_probs = gumbel_softmax(rest_logits, tau=self.tau, hard=False, dim=-1) rest_indices = rest_probs.argmax(dim=-1) rest_nodes = self.valid_atoms[rest_indices].float().view(-1, self.num_atoms - 6, 1) / 17.0 # 合并苯环和其余部分 node_feats = torch.cat([benzene_nodes, rest_nodes], dim=1) else: node_feats = benzene_nodes return node_feats # ------------------------- 增强版判别器 ------------------------- class EnhancedGraphDiscriminator(nn.Module): def init(self, node_feat_dim=1, condition_dim=2): super().init() # 图卷积层 self.conv1 = GCNConv(node_feat_dim + 1, 64) self.bn1 = nn.BatchNorm1d(64) self.conv2 = GATConv(64, 32, heads=3, concat=False) self.bn2 = nn.BatchNorm1d(32) self.conv3 = GraphConv(32, 16) self.bn3 = nn.BatchNorm1d(16) # 注意力机制 self.attention = nn.Sequential( nn.Linear(16, 8), nn.Tanh(), nn.Linear(8, 1) ) # 条件处理 self.condition_processor = nn.Sequential( nn.Linear(condition_dim, 16), nn.LeakyReLU(0.2) ) # 分类器 self.classifier = nn.Sequential( nn.Linear(16 + 16, 16), nn.LeakyReLU(0.2), nn.Linear(16, 1) ) def forward(self, data, condition): x, edge_index, batch = data.x.float(), data.edge_index, data.batch # 提取键类型特征 bond_features = torch.zeros(x.size(0), 1).to(x.device) if hasattr(data, 'bond_types') and data.bond_types is not None: bond_types = data.bond_types for i in range(edge_index.size(1)): u, v = edge_index[0, i].item(), edge_index[1, i].item() bond_type = bond_types[i] if i < len(bond_types) else 1 bond_features[u] = max(bond_features[u], bond_type) bond_features[v] = max(bond_features[v], bond_type) # 合并原子特征和键特征 x = torch.cat([x, bond_features], dim=1) # 图卷积 x = self.conv1(x, edge_index) x = self.bn1(x).relu() x = self.conv2(x, edge_index) x = self.bn2(x).relu() x = self.conv3(x, edge_index) x = self.bn3(x).relu() # 注意力池化 attn_weights = self.attention(x).squeeze() attn_weights = torch.softmax(attn_weights, dim=0) x = global_mean_pool(x * attn_weights.unsqueeze(-1), batch) # 处理条件 cond_repr = self.condition_processor(condition) # 合并图表示和条件 x = torch.cat([x, cond_repr], dim=1) # 分类 return torch.sigmoid(self.classifier(x)) # ------------------------- 训练流程（含苯环条件） ------------------------- def train_gan(dataset_path, epochs=2000, batch_size=32, lr=1e-5, device=None, generator_class=EnhancedGraphGenerator): if device is None: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") dataset = MolecularGraphDataset(dataset_path) if len(dataset) == 0: print("错误：数据集为空") return None, None def collate_fn(data_list): graphs, conditions = zip(data_list) batch_graphs = Batch.from_data_list(graphs) batch_conditions = torch.stack(conditions) return batch_graphs, batch_conditions dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn) # 创建生成器和判别器 generator = generator_class(noise_dim=16, condition_dim=2).to(device) discriminator = EnhancedGraphDiscriminator().to(device) # 调整学习率比例，让生成器学习更快 g_opt = optim.Adam(generator.parameters(), lr=lr 3, betas=(0.5, 0.999)) d_opt = optim.Adam(discriminator.parameters(), lr=lr, betas=(0.5, 0.999)) g_scheduler = optim.lr_scheduler.ReduceLROnPlateau(g_opt, 'min', patience=50, factor=0.5) d_scheduler = optim.lr_scheduler.ReduceLROnPlateau(d_opt, 'min', patience=50, factor=0.5) loss_fn = nn.BCELoss() use_amp = device.type == 'cuda' scaler = amp.GradScaler(enabled=use_amp) d_loss_history, g_loss_history = [], [] valid_mol_counts = [] fragment_counts = [] benzene_ratios = [] # 新增：记录含苯环分子比例 os.makedirs("results", exist_ok=True) os.chdir("results") for epoch in range(1, epochs + 1): generator.train() discriminator.train() total_d_loss, total_g_loss = 0.0, 0.0 for real_batch, conds in dataloader: real_batch = real_batch.to(device) conds = conds.to(device) batch_size = real_batch.num_graphs real_labels = torch.ones(batch_size, 1).to(device) fake_labels = torch.zeros(batch_size, 1).to(device) # 判别器训练 with amp.autocast(enabled=use_amp): d_real = discriminator(real_batch, conds) loss_real = loss_fn(d_real, real_labels) noise = torch.randn(batch_size, 16).to(device) # 生成苯环条件（50%概率要求含苯环） benzene_condition = torch.randint(0, 2, (batch_size,), device=device) fake_nodes = generator(noise, conds, benzene_condition).detach() fake_mols = [] for i in range(fake_nodes.shape[0]): node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] edge_index, bond_types = generate_realistic_edges_improved( node_feats, dataset.avg_edge_count, atomic_nums ) mol = build_valid_mol_improved(atomic_nums, edge_index, bond_types) if mol and mol.GetNumAtoms() > 0 and mol.GetNumBonds() > 0: fake_mols.append(mol_to_graph(mol)) if fake_mols: fake_batch = Batch.from_data_list(fake_mols).to(device) conds_subset = conds[:len(fake_mols)] d_fake = discriminator(fake_batch, conds_subset) loss_fake = loss_fn(d_fake, fake_labels[:len(fake_mols)]) d_loss = loss_real + loss_fake else: # 如果没有生成有效的分子，创建一个简单的分子作为占位符 mol = Chem.MolFromSmiles("CCO") fake_graph = mol_to_graph(mol) fake_batch = Batch.from_data_list([fake_graph]).to(device) d_fake = discriminator(fake_batch, conds[:1]) loss_fake = loss_fn(d_fake, fake_labels[:1]) d_loss = loss_real + loss_fake d_opt.zero_grad() scaler.scale(d_loss).backward() torch.nn.utils.clip_grad_norm_(discriminator.parameters(), 1.0) scaler.step(d_opt) scaler.update() total_d_loss += d_loss.item() # 生成器训练 with amp.autocast(enabled=use_amp): noise = torch.randn(batch_size, 16).to(device) benzene_condition = torch.randint(0, 2, (batch_size,), device=device) fake_nodes = generator(noise, conds, benzene_condition) fake_graphs = [] for i in range(fake_nodes.shape[0]): node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] edge_index, bond_types = generate_realistic_edges_improved( node_feats, dataset.avg_edge_count, atomic_nums ) fake_graphs.append(Data(x=node_feats, edge_index=edge_index, bond_types=bond_types)) valid_fake_graphs = [] for graph in fake_graphs: if graph.edge_index.numel() == 0: graph.edge_index = torch.tensor([[0, 0]], dtype=torch.long).t().to(device) graph.bond_types = torch.tensor([1], dtype=torch.long).to(device) valid_fake_graphs.append(graph) fake_batch = Batch.from_data_list(valid_fake_graphs).to(device) g_loss = loss_fn(discriminator(fake_batch, conds), real_labels) g_opt.zero_grad() scaler.scale(g_loss).backward() torch.nn.utils.clip_grad_norm_(generator.parameters(), 1.0) scaler.step(g_opt) scaler.update() total_g_loss += g_loss.item() avg_d_loss = total_d_loss / len(dataloader) avg_g_loss = total_g_loss / len(dataloader) d_loss_history.append(avg_d_loss) g_loss_history.append(avg_g_loss) g_scheduler.step(avg_g_loss) d_scheduler.step(avg_d_loss) if epoch % 10 == 0: generator.eval() discriminator.eval() with torch.no_grad(): num_samples = 50 noise = torch.randn(num_samples, 16).to(device) conds = torch.randn(num_samples, 2).to(device) benzene_condition = torch.ones(num_samples, dtype=torch.long, device=device) # 强制生成含苯环 fake_nodes = generator(noise, conds, benzene_condition) d_real_scores, d_fake_scores = [], [] for i in range(min(num_samples, 10)): real_idx = np.random.randint(0, len(dataset)) real_graph, real_cond = dataset[real_idx] real_batch = Batch.from_data_list([real_graph]).to(device) real_cond = real_cond.unsqueeze(0).to(device) d_real = discriminator(real_batch, real_cond) d_real_scores.append(d_real.item()) node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] edge_index, bond_types = generate_realistic_edges_improved( node_feats, dataset.avg_edge_count, atomic_nums ) mol = build_valid_mol_improved(atomic_nums, edge_index, bond_types) if mol: fake_graph = mol_to_graph(mol) if fake_graph: fake_batch = Batch.from_data_list([fake_graph]).to(device) fake_cond = conds[i].unsqueeze(0) d_fake = discriminator(fake_batch, fake_cond) d_fake_scores.append(d_fake.item()) if d_real_scores and d_fake_scores: print(f"Epoch {epoch}: D_loss={avg_d_loss:.4f}, G_loss={avg_g_loss:.4f}") print(f"D_real评分: {np.mean(d_real_scores):.4f} ± {np.std(d_real_scores):.4f}") print(f"D_fake评分: {np.mean(d_fake_scores):.4f} ± {np.std(d_fake_scores):.4f}") print(f"学习率: G={g_opt.param_groups[0]['lr']:.8f}, D={d_opt.param_groups[0]['lr']:.8f}") else: print(f"Epoch {epoch}: D_loss={avg_d_loss:.4f}, G_loss={avg_g_loss:.4f}") generator.train() if epoch % 100 == 0: torch.save(generator.state_dict(), f"generator_epoch_{epoch}.pt") torch.save(discriminator.state_dict(), f"discriminator_epoch_{epoch}.pt") generator.eval() with torch.no_grad(): num_samples = 25 noise = torch.randn(num_samples, 16).to(device) conds = torch.randn(num_samples, 2).to(device) benzene_condition = torch.ones(num_samples, dtype=torch.long, device=device) # 强制含苯环 fake_nodes = generator(noise, conds, benzene_condition) generated_mols = [] for i in range(num_samples): node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] edge_index, bond_types = generate_realistic_edges_improved( node_feats, dataset.avg_edge_count, atomic_nums ) mol = build_valid_mol_improved(atomic_nums, edge_index, bond_types) if mol and mol.GetNumAtoms() > 0 and mol.GetNumBonds() > 0: try: mol_weight = rdMolDescriptors.CalcExactMolWt(mol) logp = rdMolDescriptors.CalcCrippenDescriptors(mol)[0] tpsa = cached_calculate_tpsa(mol) generated_mols.append((mol, mol_weight, logp, tpsa)) except Exception as e: print(f"计算分子描述符时出错: {e}") # 过滤无效分子 valid_mols = filter_valid_mols([mol for mol, _, _, _ in generated_mols]) # 统计含苯环分子比例 benzene_mols = filter_benzene_mols(valid_mols) benzene_ratio = len(benzene_mols) / len(valid_mols) if valid_mols else 0 benzene_ratios.append(benzene_ratio) # 统计分子片段情况（使用改进的过滤函数） single_fragment_mols = filter_single_fragment_mols_improved(valid_mols) fragment_ratio = len(single_fragment_mols) / len(valid_mols) if valid_mols else 0 fragment_counts.append(fragment_ratio) valid_mol_counts.append(len(single_fragment_mols)) print(f"Epoch {epoch}: 生成{len(generated_mols)}个分子，初步过滤后保留{len(valid_mols)}个合法分子") print(f"Epoch {epoch}: 含苯环分子比例: {len(benzene_mols)}/{len(valid_mols)} = {benzene_ratio:.2%}") print( f"Epoch {epoch}: 单一片段分子比例: {len(single_fragment_mols)}/{len(valid_mols)} = {fragment_ratio:.2%}") if single_fragment_mols: filtered_mols = [] for mol in single_fragment_mols: try: mol_weight = rdMolDescriptors.CalcExactMolWt(mol) logp = rdMolDescriptors.CalcCrippenDescriptors(mol)[0] tpsa = cached_calculate_tpsa(mol) filtered_mols.append((mol, mol_weight, logp, tpsa)) except: continue if filtered_mols: filtered_mols.sort(key=lambda x: x[1]) mols = [mol for mol, _, _, _ in filtered_mols] legends = [ f"Mol {i + 1}\nMW: {mw:.2f}\nLogP: {logp:.2f}\nTPSA: {tpsa:.2f}" for i, (_, mw, logp, tpsa) in enumerate(filtered_mols) ] img = Draw.MolsToGridImage(mols, molsPerRow=5, subImgSize=(200, 200), legends=legends) img.save(f"generated_molecules_epoch_{epoch}.png") print(f"Epoch {epoch}: 保存{len(filtered_mols)}个单一片段分子的可视化结果") else: print(f"Epoch {epoch}: 过滤后无合法单一片段分子可显示") else: print(f"Epoch {epoch}: 未生成任何合法单一片段分子") generator.train() # 绘制损失曲线 plt.figure(figsize=(10, 5)) plt.plot(d_loss_history, label='Discriminator Loss') plt.plot(g_loss_history, label='Generator Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.title('GAN Loss Curve') plt.legend() plt.savefig('gan_loss_curve.png') plt.close() # 绘制合法分子数量曲线 plt.figure(figsize=(10, 5)) plt.plot([i * 100 for i in range(1, len(valid_mol_counts) + 1)], valid_mol_counts, label='Valid Single-Fragment Molecules') plt.xlabel('Epoch') plt.ylabel('Number of Valid Molecules') plt.title('Number of Valid Single-Fragment Molecules Generated per Epoch') plt.legend() plt.savefig('valid_molecules_curve.png') plt.close() # 绘制分子片段比例曲线 plt.figure(figsize=(10, 5)) plt.plot([i * 100 for i in range(1, len(fragment_counts) + 1)], fragment_counts, label='Single-Fragment Ratio') plt.xlabel('Epoch') plt.ylabel('Single-Fragment Molecules Ratio') plt.title('Ratio of Single-Fragment Molecules in Generated Molecules') plt.legend() plt.savefig('fragment_ratio_curve.png') plt.close() # 绘制苯环比例曲线 plt.figure(figsize=(10, 5)) plt.plot([i * 100 for i in range(1, len(benzene_ratios) + 1)], benzene_ratios, label='Benzene Ring Ratio') plt.xlabel('Epoch') plt.ylabel('Benzene Ring Molecules Ratio') plt.title('Ratio of Molecules Containing Benzene Rings') plt.legend() plt.savefig('benzene_ratio_curve.png') plt.close() return generator, discriminator # ------------------------- 参考分子处理与批量生成（改进版）------------------------- def process_reference_smiles(reference_smiles): ref_graph = smiles_to_graph(reference_smiles) if ref_graph is None: raise ValueError("参考分子SMILES转换图结构失败，请检查SMILES合法性") ref_concentration = 1.0 ref_efficiency = 0.8 ref_condition = torch.tensor([ref_concentration, ref_efficiency], dtype=torch.float32) return ref_graph, ref_condition def generate_molecules_with_reference_improved(generator, reference_smiles, num_samples=1000, noise_type="gaussian", force_benzene=True, device=None): if device is None: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ref_graph, ref_condition = process_reference_smiles(reference_smiles) ref_condition_batch = ref_condition.unsqueeze(0).repeat(num_samples, 1).to(device) if noise_type.lower() == "gaussian": noise = torch.randn(num_samples, 16).to(device) elif noise_type.lower() == "uniform": noise = 2 * torch.rand(num_samples, 16).to(device) - 1 else: raise ValueError("噪声类型必须是 'gaussian' 或 'uniform'") generator.eval() generated_mols = [] # 强制生成含苯环分子 benzene_condition = torch.ones(num_samples, dtype=torch.long, device=device) if force_benzene else \ torch.randint(0, 2, (num_samples,), device=device) with torch.no_grad(): fake_nodes = generator(noise, ref_condition_batch, benzene_condition) for i in range(num_samples): node_feats = fake_nodes[i] atomic_nums = (node_feats.squeeze() * 17).cpu().numpy().round().astype(int) atomic_nums = validate_atomic_nums(atomic_nums) atomic_nums = [int(num) for num in atomic_nums] # 使用改进的边生成函数 edge_index, bond_types = generate_realistic_edges_improved( node_feats, 20, atomic_nums ) # 使用改进的分子构建函数 mol = build_valid_mol_improved(atomic_nums, edge_index, bond_types) if mol and mol.GetNumAtoms() > 0 and mol.GetNumBonds() > 0: generated_mols.append(mol) # 过滤无效分子 valid_mols = filter_valid_mols(generated_mols) print(f"使用{noise_type}噪声，基于参考分子生成 {num_samples} 个分子，初步过滤后有效分子: {len(valid_mols)}") # 过滤含苯环的分子（如果强制要求） if force_benzene: benzene_mols = filter_benzene_mols(valid_mols) print(f"进一步过滤后，含苯环分子数量: {len(benzene_mols)}") print(f"含苯环分子比例: {len(benzene_mols) / len(valid_mols):.2%}") valid_mols = benzene_mols # 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检测苯环 has_benzene = has_benzene_ring(mol) properties.append({ 'SMILES': Chem.MolToSmiles(mol), 'MW': mol_weight, 'LogP': logp, 'TPSA': tpsa, 'HBA': hba, 'HBD': hbd, 'RotBonds': rot_bonds, 'N_count': n_count, 'O_count': o_count, 'S_count': s_count, 'P_count': p_count, 'FragmentCount': fragment_count, 'HasBenzene': has_benzene }) except Exception as e: print(f"计算分子属性时出错: {e}") continue return properties def save_molecules(mols, prefix="generated", noise_type="gaussian"): if not mols: print("没有分子可保存") return subdir = f"{prefix}_{noise_type}" os.makedirs(subdir, exist_ok=True) if len(mols) <= 100: legends = [f"Mol {i + 1}" for i in range(len(mols))] img = Draw.MolsToGridImage(mols, molsPerRow=5, subImgSize=(300, 300), legends=legends) img.save(f"{subdir}/{prefix}_{noise_type}_molecules.png") properties = calculate_molecular_properties(mols) df = pd.DataFrame(properties) df.to_csv(f"{subdir}/{prefix}_{noise_type}_properties.csv", index=False) with open(f"{subdir}/{prefix}_{noise_type}_smiles.smi", "w") as f: for props in properties: f.write(f"{props['SMILES']}\n") print(f"已保存 {len(mols)} 个单一片段分子到目录: {subdir}") # 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------------------------- 新增：分子可视化工具 ------------------------- def visualize_molecules_grid(molecules, num_per_row=5, filename="molecules_grid.png", legends=None): """创建高质量的分子网格可视化图""" if not molecules: print("没有分子可可视化") return if legends is None: legends = [f"Molecule {i + 1}" for i in range(len(molecules))] try: img = Draw.MolsToGridImage( molecules, molsPerRow=num_per_row, subImgSize=(300, 300), legends=legends, useSVG=False, highlightAtomLists=None, highlightBondLists=None ) img.save(filename) print(f"分子网格图已保存至: {filename}") return img except Exception as e: print(f"生成分子网格图时出错: {e}") return None # ------------------------- 主函数 ------------------------- def main(): print("=" * 80) print("基于合法SMILES的分子生成GAN系统（改进版）") print("=" * 80) dataset_path = "D:\python\pythonProject1\DATA\Inhibitor1368_data.xlsx" if not os.path.exists(dataset_path): print(f"错误：数据集文件 '{dataset_path}' 不存在！") exit(1) print(f"开始加载数据集: {dataset_path}") print("=" * 80) print("开始训练分子生成GAN...") generator, discriminator = train_gan( dataset_path=dataset_path, epochs=200, batch_size=32, lr=1e-5, ) print("=" * 80) # 设置参考缓蚀剂分子（确保是单一片段含苯环的有效分子） reference_smiles = "NCCNCc1ccc(O)c2ncccc12" if generator: print("训练完成！模型和生成结果已保存") print("生成的分子可视化结果在'results'目录") print("损失曲线已保存为'gan_loss_curve.png'") print("合法分子数量曲线已保存为'valid_molecules_curve.png'") print("分子片段比例曲线已保存为'fragment_ratio_curve.png'") print("苯环比例曲线已保存为'benzene_ratio_curve.png'") # 基于参考分子生成1000个新分子（高斯噪声，强制含苯环） print("\n开始基于参考分子生成新分子（高斯噪声，强制含苯环）...") gaussian_mols = generate_molecules_with_reference_improved( generator, reference_smiles, num_samples=1000, noise_type="gaussian", force_benzene=True # 强制生成含苯环分子 ) save_molecules(gaussian_mols, prefix="ref_based", noise_type="gaussian_benzene") # 可视化最佳分子 if gaussian_mols: # 计算每个分子的QED分数 qed_scores = [] for mol in gaussian_mols: try: qed_scores.append(rdMolDescriptors.CalcQED(mol)) except: qed_scores.append(0) # 按QED分数排序 sorted_indices = sorted(range(len(qed_scores)), key=lambda i: qed_scores[i], reverse=True) top_molecules = [gaussian_mols[i] for i in sorted_indices[:25]] top_legends = [f"QED: {qed_scores[i]:.3f}" for i in sorted_indices[:25]] # 可视化 visualize_molecules_grid( top_molecules, num_per_row=5, filename="top_molecules_by_qed.png", legends=top_legends ) print("已生成并保存最佳分子的可视化结果") # 基于参考分子生成1000个新分子（均匀噪声，强制含苯环） print("\n开始基于参考分子生成新分子（均匀噪声，强制含苯环）...") uniform_mols = generate_molecules_with_reference_improved( generator, reference_smiles, num_samples=1000, noise_type="uniform", force_benzene=True # 强制生成含苯环分子 ) save_molecules(uniform_mols, prefix="ref_based", noise_type="uniform_benzene") # 分析两种噪声生成的分子差异 if gaussian_mols and uniform_mols: analyze_generated_molecules(gaussian_mols, uniform_mols) else: print("训练过程中未生成合法分子，请检查数据") print("=" * 80) if name == "main": main()

def train_gan(dataset_path, epochs=2000, batch_size=32, lr=1e-5, device=None): # ... [原有训练代码] ... # 在生成器训练循环中添加连通性奖励 with amp.autocast(enabled=use_amp): # ... [原有生成器...

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解释下面代码的作用：" def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1"

代码解释： def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

解释代码: def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

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解释下面代码的作用：" def __len__(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1"

代码解释： def __len__(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

解释代码: def __len__(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

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解释代码： def __len__(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

解释代码 def __len__(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

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解释下面代码的作用：" def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1"

代码解释： def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

解释代码: def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

解释代码： def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1

解释代码 def len(self): remain = len(self.dataset) % self.batch_size if self.drop_last or not remain: return len(self.dataset) // self.batch_size else: return len(self.dataset) // self.batch_size + 1