model/utils.py

import torch
import math
import torch.nn as nn
from rdkit import Chem
from rdkit import rdBase
rdBase.DisableLog('rdApp.*')

# Split SMILES into words
def split(sm):
    '''
    function: Split SMILES into words. Care for Cl, Br, Si, Se, Na etc.
    input: A SMILES
    output: A string with space between words
    '''
    arr = []
    i = 0
    while i < len(sm)-1:
        if not sm[i] in ['%', 'C', 'B', 'S', 'N', 'R', 'X', 'L', 'A', 'M', \
                        'T', 'Z', 's', 't', 'H', '+', '-', 'K', 'F']:
            arr.append(sm[i])
            i += 1
        elif sm[i]=='%':
            arr.append(sm[i:i+3])
            i += 3
        elif sm[i]=='C' and sm[i+1]=='l':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='C' and sm[i+1]=='a':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='C' and sm[i+1]=='u':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='B' and sm[i+1]=='r':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='B' and sm[i+1]=='e':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='B' and sm[i+1]=='a':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='B' and sm[i+1]=='i':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='S' and sm[i+1]=='i':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='S' and sm[i+1]=='e':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='S' and sm[i+1]=='r':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='N' and sm[i+1]=='a':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='N' and sm[i+1]=='i':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='R' and sm[i+1]=='b':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='R' and sm[i+1]=='a':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='X' and sm[i+1]=='e':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='L' and sm[i+1]=='i':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='A' and sm[i+1]=='l':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='A' and sm[i+1]=='s':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='A' and sm[i+1]=='g':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='A' and sm[i+1]=='u':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='M' and sm[i+1]=='g':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='M' and sm[i+1]=='n':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='T' and sm[i+1]=='e':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='Z' and sm[i+1]=='n':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='s' and sm[i+1]=='i':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='s' and sm[i+1]=='e':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='t' and sm[i+1]=='e':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='H' and sm[i+1]=='e':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='+' and sm[i+1]=='2':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='+' and sm[i+1]=='3':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='+' and sm[i+1]=='4':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='-' and sm[i+1]=='2':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='-' and sm[i+1]=='3':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='-' and sm[i+1]=='4':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='K' and sm[i+1]=='r':
            arr.append(sm[i:i+2])
            i += 2
        elif sm[i]=='F' and sm[i+1]=='e':
            arr.append(sm[i:i+2])
            i += 2
        else:
            arr.append(sm[i])
            i += 1
    if i == len(sm)-1:
        arr.append(sm[i])
    return ' '.join(arr) 

# 活性化関数
class GELU(nn.Module):
    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))

# 位置情報を考慮したFFN
class PositionwiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff, dropout=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)
        self.activation = GELU()

    def forward(self, x):
        return self.w_2(self.dropout(self.activation(self.w_1(x))))
    
# 正規化層
class LayerNorm(nn.Module):
    def __init__(self, features, eps=1e-6):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2


class SublayerConnection(nn.Module):
    def __init__(self, size, dropout):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, sublayer):
        return x + self.dropout(sublayer(self.norm(x)))

# Sample SMILES from probablistic distribution
def sample(msms):
    ret = []
    for msm in msms:
        ret.append(torch.multinomial(msm.exp(), 1).squeeze())
    return torch.stack(ret)

def validity(smiles):
    loss = 0
    for sm in smiles:
        mol = Chem.MolFromSmiles(sm)
        if mol is None:
            loss += 1
    return 1-loss/len(smiles)