enable digress in workflow interface api

Signed-off-by: kta-intel <kevin.ta@intel.com>

openfl-tutorials/experimental/DiGress/digress/analysis/rdkit_functions.py

@@ -0,0 +1,339 @@
+# Copyright (c) 2012-2022 Clement Vignac, Igor Krawczuk, Antoine Siraudin
+# source: https://github.com/cvignac/DiGress/
+
+import numpy as np
+import torch
+import re
+# import wandb
+try:
+    from rdkit import Chem
+    print("Found rdkit, all good")
+except ModuleNotFoundError as e:
+    use_rdkit = False
+    from warnings import warn
+    warn("Didn't find rdkit, this will fail")
+    assert use_rdkit, "Didn't find rdkit"
+
+from rdkit import RDLogger
+RDLogger.DisableLog('rdApp.*')
+
+allowed_bonds = {'H': 1, 'C': 4, 'N': 3, 'O': 2, 'F': 1, 'B': 3, 'Al': 3, 'Si': 4, 'P': [3, 5],
+                 'S': 4, 'Cl': 1, 'As': 3, 'Br': 1, 'I': 1, 'Hg': [1, 2], 'Bi': [3, 5], 'Se': [2, 4, 6]}
+bond_dict = [None, Chem.rdchem.BondType.SINGLE, Chem.rdchem.BondType.DOUBLE, Chem.rdchem.BondType.TRIPLE,
+                 Chem.rdchem.BondType.AROMATIC]
+ATOM_VALENCY = {6: 4, 7: 3, 8: 2, 9: 1, 15: 3, 16: 2, 17: 1, 35: 1, 53: 1}
+
+
+class BasicMolecularMetrics(object):
+    def __init__(self, dataset_info, train_smiles=None):
+        self.atom_decoder = dataset_info.atom_decoder
+        self.dataset_info = dataset_info
+
+        # Retrieve dataset smiles only for qm9 currently.
+        self.dataset_smiles_list = train_smiles
+
+    def compute_validity(self, generated):
+        """ generated: list of couples (positions, atom_types)"""
+        valid = []
+        num_components = []
+        all_smiles = []
+        for graph in generated:
+            atom_types, edge_types = graph
+            mol = build_molecule(atom_types, edge_types, self.dataset_info.atom_decoder)
+            smiles = mol2smiles(mol)
+            try:
+                mol_frags = Chem.rdmolops.GetMolFrags(mol, asMols=True, sanitizeFrags=True)
+                num_components.append(len(mol_frags))
+            except:
+                pass
+            if smiles is not None:
+                try:
+                    mol_frags = Chem.rdmolops.GetMolFrags(mol, asMols=True, sanitizeFrags=True)
+                    largest_mol = max(mol_frags, default=mol, key=lambda m: m.GetNumAtoms())
+                    smiles = mol2smiles(largest_mol)
+                    valid.append(smiles)
+                    all_smiles.append(smiles)
+                except Chem.rdchem.AtomValenceException:
+                    print("Valence error in GetmolFrags")
+                    all_smiles.append(None)
+                except Chem.rdchem.KekulizeException:
+                    print("Can't kekulize molecule")
+                    all_smiles.append(None)
+            else:
+                all_smiles.append(None)
+
+        return valid, len(valid) / len(generated), np.array(num_components), all_smiles
+
+    def compute_uniqueness(self, valid):
+        """ valid: list of SMILES strings."""
+        return list(set(valid)), len(set(valid)) / len(valid)
+
+    def compute_novelty(self, unique):
+        num_novel = 0
+        novel = []
+        if self.dataset_smiles_list is None:
+            print("Dataset smiles is None, novelty computation skipped")
+            return 1, 1
+        for smiles in unique:
+            if smiles not in self.dataset_smiles_list:
+                novel.append(smiles)
+                num_novel += 1
+        return novel, num_novel / len(unique)
+
+    def compute_relaxed_validity(self, generated):
+        valid = []
+        for graph in generated:
+            atom_types, edge_types = graph
+            mol = build_molecule_with_partial_charges(atom_types, edge_types, self.dataset_info.atom_decoder)
+            smiles = mol2smiles(mol)
+            if smiles is not None:
+                try:
+                    mol_frags = Chem.rdmolops.GetMolFrags(mol, asMols=True, sanitizeFrags=True)
+                    largest_mol = max(mol_frags, default=mol, key=lambda m: m.GetNumAtoms())
+                    smiles = mol2smiles(largest_mol)
+                    valid.append(smiles)
+                except Chem.rdchem.AtomValenceException:
+                    print("Valence error in GetmolFrags")
+                except Chem.rdchem.KekulizeException:
+                    print("Can't kekulize molecule")
+        return valid, len(valid) / len(generated)
+
+    def evaluate(self, generated):
+        """ generated: list of pairs (positions: n x 3, atom_types: n [int])
+            the positions and atom types should already be masked. """
+        valid, validity, num_components, all_smiles = self.compute_validity(generated)
+        nc_mu = num_components.mean() if len(num_components) > 0 else 0
+        nc_min = num_components.min() if len(num_components) > 0 else 0
+        nc_max = num_components.max() if len(num_components) > 0 else 0
+        # print(f"Validity over {len(generated)} molecules: {validity * 100 :.2f}%")
+        # print(f"Number of connected components of {len(generated)} molecules: min:{nc_min:.2f} mean:{nc_mu:.2f} max:{nc_max:.2f}")
+
+        relaxed_valid, relaxed_validity = self.compute_relaxed_validity(generated)
+        # print(f"Relaxed validity over {len(generated)} molecules: {relaxed_validity * 100 :.2f}%")
+        if relaxed_validity > 0:
+            unique, uniqueness = self.compute_uniqueness(relaxed_valid)
+            # print(f"Uniqueness over {len(relaxed_valid)} valid molecules: {uniqueness * 100 :.2f}%")
+
+            if self.dataset_smiles_list is not None:
+                _, novelty = self.compute_novelty(unique)
+                # print(f"Novelty over {len(unique)} unique valid molecules: {novelty * 100 :.2f}%")
+            else:
+                novelty = -1.0
+        else:
+            novelty = -1.0
+            uniqueness = 0.0
+            unique = []
+        return ([validity, relaxed_validity, uniqueness, novelty], unique,
+                dict(nc_min=nc_min, nc_max=nc_max, nc_mu=nc_mu), all_smiles)
+
+
+def mol2smiles(mol):
+    try:
+        Chem.SanitizeMol(mol)
+    except ValueError:
+        return None
+    return Chem.MolToSmiles(mol)
+
+
+def build_molecule(atom_types, edge_types, atom_decoder, verbose=False):
+    if verbose:
+        print("building new molecule")
+
+    mol = Chem.RWMol()
+    for atom in atom_types:
+        a = Chem.Atom(atom_decoder[atom.item()])
+        mol.AddAtom(a)
+        if verbose:
+            print("Atom added: ", atom.item(), atom_decoder[atom.item()])
+
+    edge_types = torch.triu(edge_types)
+    all_bonds = torch.nonzero(edge_types)
+    for i, bond in enumerate(all_bonds):
+        if bond[0].item() != bond[1].item():
+            mol.AddBond(bond[0].item(), bond[1].item(), bond_dict[edge_types[bond[0], bond[1]].item()])
+            if verbose:
+                print("bond added:", bond[0].item(), bond[1].item(), edge_types[bond[0], bond[1]].item(),
+                      bond_dict[edge_types[bond[0], bond[1]].item()] )
+    return mol
+
+
+def build_molecule_with_partial_charges(atom_types, edge_types, atom_decoder, verbose=False):
+    if verbose:
+        print("\nbuilding new molecule")
+
+    mol = Chem.RWMol()
+    for atom in atom_types:
+        a = Chem.Atom(atom_decoder[atom.item()])
+        mol.AddAtom(a)
+        if verbose:
+            print("Atom added: ", atom.item(), atom_decoder[atom.item()])
+    edge_types = torch.triu(edge_types)
+    all_bonds = torch.nonzero(edge_types)
+
+    for i, bond in enumerate(all_bonds):
+        if bond[0].item() != bond[1].item():
+            mol.AddBond(bond[0].item(), bond[1].item(), bond_dict[edge_types[bond[0], bond[1]].item()])
+            if verbose:
+                print("bond added:", bond[0].item(), bond[1].item(), edge_types[bond[0], bond[1]].item(),
+                      bond_dict[edge_types[bond[0], bond[1]].item()])
+            # add formal charge to atom: e.g. [O+], [N+], [S+]
+            # not support [O-], [N-], [S-], [NH+] etc.
+            flag, atomid_valence = check_valency(mol)
+            if verbose:
+                print("flag, valence", flag, atomid_valence)
+            if flag:
+                continue
+            else:
+                assert len(atomid_valence) == 2
+                idx = atomid_valence[0]
+                v = atomid_valence[1]
+                an = mol.GetAtomWithIdx(idx).GetAtomicNum()
+                if verbose:
+                    print("atomic num of atom with a large valence", an)
+                if an in (7, 8, 16) and (v - ATOM_VALENCY[an]) == 1:
+                    mol.GetAtomWithIdx(idx).SetFormalCharge(1)
+                    # print("Formal charge added")
+    return mol
+
+
+# Functions from GDSS
+def check_valency(mol):
+    try:
+        Chem.SanitizeMol(mol, sanitizeOps=Chem.SanitizeFlags.SANITIZE_PROPERTIES)
+        return True, None
+    except ValueError as e:
+        e = str(e)
+        p = e.find('#')
+        e_sub = e[p:]
+        atomid_valence = list(map(int, re.findall(r'\d+', e_sub)))
+        return False, atomid_valence
+
+
+def correct_mol(m):
+    # xsm = Chem.MolToSmiles(x, isomericSmiles=True)
+    mol = m
+
+    #####
+    no_correct = False
+    flag, _ = check_valency(mol)
+    if flag:
+        no_correct = True
+
+    while True:
+        flag, atomid_valence = check_valency(mol)
+        if flag:
+            break
+        else:
+            assert len(atomid_valence) == 2
+            idx = atomid_valence[0]
+            v = atomid_valence[1]
+            queue = []
+            check_idx = 0
+            for b in mol.GetAtomWithIdx(idx).GetBonds():
+                type = int(b.GetBondType())
+                queue.append((b.GetIdx(), type, b.GetBeginAtomIdx(), b.GetEndAtomIdx()))
+                if type == 12:
+                    check_idx += 1
+            queue.sort(key=lambda tup: tup[1], reverse=True)
+
+            if queue[-1][1] == 12:
+                return None, no_correct
+            elif len(queue) > 0:
+                start = queue[check_idx][2]
+                end = queue[check_idx][3]
+                t = queue[check_idx][1] - 1
+                mol.RemoveBond(start, end)
+                if t >= 1:
+                    mol.AddBond(start, end, bond_dict[t])
+    return mol, no_correct
+
+
+def valid_mol_can_with_seg(m, largest_connected_comp=True):
+    if m is None:
+        return None
+    sm = Chem.MolToSmiles(m, isomericSmiles=True)
+    if largest_connected_comp and '.' in sm:
+        vsm = [(s, len(s)) for s in sm.split('.')]  # 'C.CC.CCc1ccc(N)cc1CCC=O'.split('.')
+        vsm.sort(key=lambda tup: tup[1], reverse=True)
+        mol = Chem.MolFromSmiles(vsm[0][0])
+    else:
+        mol = Chem.MolFromSmiles(sm)
+    return mol
+
+
+if __name__ == '__main__':
+    smiles_mol = 'C1CCC1'
+    print("Smiles mol %s" % smiles_mol)
+    chem_mol = Chem.MolFromSmiles(smiles_mol)
+    block_mol = Chem.MolToMolBlock(chem_mol)
+    print("Block mol:")
+    print(block_mol)
+
+use_rdkit = True
+
+
+def check_stability(atom_types, edge_types, dataset_info, debug=False,atom_decoder=None):
+    if atom_decoder is None:
+        atom_decoder = dataset_info.atom_decoder
+
+    n_bonds = np.zeros(len(atom_types), dtype='int')
+
+    for i in range(len(atom_types)):
+        for j in range(i + 1, len(atom_types)):
+            n_bonds[i] += abs((edge_types[i, j] + edge_types[j, i])/2)
+            n_bonds[j] += abs((edge_types[i, j] + edge_types[j, i])/2)
+    n_stable_bonds = 0
+    for atom_type, atom_n_bond in zip(atom_types, n_bonds):
+        possible_bonds = allowed_bonds[atom_decoder[atom_type]]
+        if type(possible_bonds) == int:
+            is_stable = possible_bonds == atom_n_bond
+        else:
+            is_stable = atom_n_bond in possible_bonds
+        if not is_stable and debug:
+            print("Invalid bonds for molecule %s with %d bonds" % (atom_decoder[atom_type], atom_n_bond))
+        n_stable_bonds += int(is_stable)
+
+    molecule_stable = n_stable_bonds == len(atom_types)
+    return molecule_stable, n_stable_bonds, len(atom_types)
+
+
+def compute_molecular_metrics(molecule_list, train_smiles, dataset_info):
+    """ molecule_list: (dict) """
+
+    if not dataset_info.remove_h:
+        print(f'Analyzing molecule stability...')
+
+        molecule_stable = 0
+        nr_stable_bonds = 0
+        n_atoms = 0
+        n_molecules = len(molecule_list)
+
+        for i, mol in enumerate(molecule_list):
+            atom_types, edge_types = mol
+
+            validity_results = check_stability(atom_types, edge_types, dataset_info)
+
+            molecule_stable += int(validity_results[0])
+            nr_stable_bonds += int(validity_results[1])
+            n_atoms += int(validity_results[2])
+
+        # Validity
+        fraction_mol_stable = molecule_stable / float(n_molecules)
+        fraction_atm_stable = nr_stable_bonds / float(n_atoms)
+        validity_dict = {'mol_stable': fraction_mol_stable, 'atm_stable': fraction_atm_stable}
+        # if wandb.run:
+        #     wandb.log(validity_dict)
+    else:
+        validity_dict = {'mol_stable': -1, 'atm_stable': -1}
+
+    metrics = BasicMolecularMetrics(dataset_info, train_smiles)
+    rdkit_metrics = metrics.evaluate(molecule_list)
+    all_smiles = rdkit_metrics[-1]
+    # if wandb.run:
+    #     nc = rdkit_metrics[-2]
+    #     dic = {'Validity': rdkit_metrics[0][0], 'Relaxed Validity': rdkit_metrics[0][1],
+    #            'Uniqueness': rdkit_metrics[0][2], 'Novelty': rdkit_metrics[0][3],
+    #            'nc_max': nc['nc_max'], 'nc_mu': nc['nc_mu']}
+    #     wandb.log(dic)
+
+    return validity_dict, rdkit_metrics, all_smiles