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Delete ssretro_template.py
Browse files- ssretro_template.py +0 -93
ssretro_template.py
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from rdkit.Chem import AllChem
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from mhnreact.data import load_dataset_from_csv
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from mhnreact.molutils import convert_smiles_to_fp
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from rdchiral.main import rdchiralRun, rdchiralReaction, rdchiralReactants
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import torch
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reaction_superclass_names = {
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1: 'Heteroatom alkylation and arylation',
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2: 'Acylation and related processes',
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3: 'C-C bond formation',
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4: 'Heterocycle formation', # TODO check
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5: 'Protections',
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6: 'Deprotections',
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7: 'Reductions',
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8: 'Oxidations',
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9: 'Functional group interconversoin (FGI)',
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10: 'Functional group addition (FGA)'
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}
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def getTemplateApplicabilityMatrix(t, fp_size=8096, fp_type='pattern'):
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only_left_side_of_templates = list(map(lambda k: k.split('>>')[0], t.values()))
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return convert_smiles_to_fp(only_left_side_of_templates, is_smarts=True, which=fp_type, fp_size=fp_size)
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def FPF(smi, templates, fp_size=8096, fp_type='pattern'):
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"""Fingerprint-Filter for applicability"""
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tfp = getTemplateApplicabilityMatrix(templates, fp_size=fp_size, fp_type=fp_type)
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if not isinstance(smi, list):
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smi = [smi]
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mfp = convert_smiles_to_fp(smi, which=fp_type, fp_size=fp_size)
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applicable = ((tfp & mfp).sum(1) == (tfp.sum(1)))
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return applicable
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def ssretro(target_smiles: str, clf, num_paths=5, try_max_temp=10, viz=False, use_FPF=False):
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"""single-step-retrosynthesis"""
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X, y, t, test_reactants_can = load_dataset_from_csv('data/USPTO_50k_MHN_prepro.csv.gz', ssretroeval=True)
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if hasattr(clf, 'templates'):
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if clf.X is None:
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clf.X = clf.template_encoder(clf.templates)
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preds = clf.forward_smiles([target_smiles])
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if use_FPF:
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appl = FPF(target_smiles, t)
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preds = preds * torch.tensor(appl)
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preds = clf.softmax(preds)
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idxs = preds.argsort().detach().numpy().flatten()[::-1]
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preds = preds.detach().numpy().flatten()
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try:
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prod_rct = rdchiralReactants(target_smiles)
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except:
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print('target_smiles', target_smiles, 'not computebale')
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return []
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reactions = []
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i = 0
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while len(reactions) < num_paths and (i < try_max_temp):
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resu = []
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while (not len(resu)) and (i < try_max_temp): # continue
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# print(i, end=' \r')
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try:
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rxn = rdchiralReaction(t[idxs[i]])
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resu = rdchiralRun(rxn, prod_rct, keep_mapnums=True, combine_enantiomers=True, return_mapped=True)
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except:
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resu = ['err']
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i += 1
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if len(resu) == 2: # if there is a result
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res, mapped_res = resu
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rs = [AllChem.MolToSmiles(prod_rct.reactants) + '>>' + k[0] for k in list(mapped_res.values())]
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for r in rs:
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di = {
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# 'template_used': t[idxs[i]],
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# 'template_idx': idxs[i],
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'template_rank': i + 1, # get the acutal rank, not the one without non-executable
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'reaction': r,
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# 'reaction_canonical': canonicalize_template(r),
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'prob': preds[idxs[i]] * 100
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# 'template_class': reaction_superclass_names[
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# df[df.reaction_smarts == t[idxs[i]]]["class"].unique()[0]]
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}
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# di['template_num_train_samples'] = (y['train'] == di['template_idx']).sum()
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reactions.append(di)
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if viz:
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for r in rs:
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print('with template #', idxs[i], t[idxs[i]])
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# smarts2svg(r, useSmiles=True, highlightByReactant=True);
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return reactions
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