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| from rdkit.Chem import AllChem | |
| from mhnreact.data import load_dataset_from_csv | |
| from mhnreact.molutils import convert_smiles_to_fp | |
| from rdchiral.main import rdchiralRun, rdchiralReaction, rdchiralReactants | |
| import torch | |
| reaction_superclass_names = { | |
| 1: 'Heteroatom alkylation and arylation', | |
| 2: 'Acylation and related processes', | |
| 3: 'C-C bond formation', | |
| 4: 'Heterocycle formation', # TODO check | |
| 5: 'Protections', | |
| 6: 'Deprotections', | |
| 7: 'Reductions', | |
| 8: 'Oxidations', | |
| 9: 'Functional group interconversoin (FGI)', | |
| 10: 'Functional group addition (FGA)' | |
| } | |
| def getTemplateApplicabilityMatrix(t, fp_size=8096, fp_type='pattern'): | |
| only_left_side_of_templates = list(map(lambda k: k.split('>>')[0], t.values())) | |
| return convert_smiles_to_fp(only_left_side_of_templates, is_smarts=True, which=fp_type, fp_size=fp_size) | |
| def FPF(smi, templates, fp_size=8096, fp_type='pattern'): | |
| """Fingerprint-Filter for applicability""" | |
| tfp = getTemplateApplicabilityMatrix(templates, fp_size=fp_size, fp_type=fp_type) | |
| if not isinstance(smi, list): | |
| smi = [smi] | |
| mfp = convert_smiles_to_fp(smi, which=fp_type, fp_size=fp_size) | |
| applicable = ((tfp & mfp).sum(1) == (tfp.sum(1))) | |
| return applicable | |
| def ssretro(target_smiles: str, clf, num_paths=5, try_max_temp=10, viz=False, use_FPF=False): | |
| """single-step-retrosynthesis""" | |
| X, y, t, test_reactants_can = load_dataset_from_csv('data/USPTO_50k_MHN_prepro.csv.gz', ssretroeval=True) | |
| if hasattr(clf, 'templates'): | |
| if clf.X is None: | |
| clf.X = clf.template_encoder(clf.templates) | |
| preds = clf.forward_smiles([target_smiles]) | |
| if use_FPF: | |
| appl = FPF(target_smiles, t) | |
| preds = preds * torch.tensor(appl) | |
| preds = clf.softmax(preds) | |
| idxs = preds.argsort().detach().numpy().flatten()[::-1] | |
| preds = preds.detach().numpy().flatten() | |
| try: | |
| prod_rct = rdchiralReactants(target_smiles) | |
| except: | |
| print('target_smiles', target_smiles, 'not computebale') | |
| return [] | |
| reactions = [] | |
| i = 0 | |
| while len(reactions) < num_paths and (i < try_max_temp): | |
| resu = [] | |
| while (not len(resu)) and (i < try_max_temp): # continue | |
| # print(i, end=' \r') | |
| try: | |
| rxn = rdchiralReaction(t[idxs[i]]) | |
| resu = rdchiralRun(rxn, prod_rct, keep_mapnums=True, combine_enantiomers=True, return_mapped=True) | |
| except: | |
| resu = ['err'] | |
| i += 1 | |
| if len(resu) == 2: # if there is a result | |
| res, mapped_res = resu | |
| rs = [AllChem.MolToSmiles(prod_rct.reactants) + '>>' + k[0] for k in list(mapped_res.values())] | |
| for r in rs: | |
| di = { | |
| # 'template_used': t[idxs[i]], | |
| # 'template_idx': idxs[i], | |
| 'template_rank': i + 1, # get the acutal rank, not the one without non-executable | |
| 'reaction': r, | |
| # 'reaction_canonical': canonicalize_template(r), | |
| 'prob': preds[idxs[i]] * 100 | |
| # 'template_class': reaction_superclass_names[ | |
| # df[df.reaction_smarts == t[idxs[i]]]["class"].unique()[0]] | |
| } | |
| # di['template_num_train_samples'] = (y['train'] == di['template_idx']).sum() | |
| reactions.append(di) | |
| if viz: | |
| for r in rs: | |
| print('with template #', idxs[i], t[idxs[i]]) | |
| # smarts2svg(r, useSmiles=True, highlightByReactant=True); | |
| return reactions | |