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