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