Drug/Target Encoder¶
Drug encoding¶
Drug Encodings |
Description |
---|---|
Morgan |
Extended-Connectivity Fingerprints |
Pubchem |
Pubchem Substructure-based Fingerprints |
Daylight |
Daylight-type fingerprints |
rdkit_2d_normalized |
Normalized Descriptastorus |
CNN |
Convolutional Neural Network on SMILES |
CNN_RNN |
A GRU/LSTM on top of a CNN on SMILES |
Transformer |
Transformer Encoder on ESPF |
MPNN |
Message-passing neural network |
Target encoding¶
Target Encodings |
Description |
---|---|
AAC |
Amino acid composition up to 3-mers |
PseudoAAC |
Pseudo amino acid composition |
Conjoint_triad |
Conjoint triad features |
Quasi-seq |
Quasi-sequence order descriptor |
CNN |
Convolutional Neural Network on target seq |
CNN_RNN |
A GRU/LSTM on top of a CNN on target seq |
Transformer |
Transformer Encoder on ESPF |
Encoder Model¶
Encoder Model |
Description |
---|---|
CNN |
Convolutional Neural Network on SMILES |
CNN_RNN |
A GRU/LSTM on top of a CNN on SMILES |
Transformer |
Transformer Encoder on SMILES |
MPNN |
Message Passing Neural Network on Molecular Graph |
MLP |
MultiLayer Perceptron on fix-dim feature vector |
Technical Details¶
First, we describe the common modules we import in DeepPurpose.
import torch
from torch.autograd import Variable
import torch.nn.functional as F
from torch import nn
import numpy as np
import pandas as pd
Links of details of various encoders