MolGpKa

Fast and accurate prediction of the pKa values of small molecules is important in the drug discovery process since the ionization state of a drug has significant influence on its activity and ADME-Tox properties. MolGpKa is a tool for pKa prediction using graph-convolutional neural network model. The model works by learning pKa related chemical patterns automatically and building reliable predictors with learned features.

Requirements

• Python 3.6
• Pytorch >=1.4
• Pytorch-geometric (https://github.com/rusty1s/pytorch_geometric)
• RDKit (http://www.rdkit.org/docs/Install.html)
• py3Dmol
• sklearn 0.21.3
• numpy 1.18.1
• pandas 0.25.3
• pickle

Usage

Using trained model for pKa prediction

example.ipynb is an example notebook for using MolGpKa, model weights file are located in models.

Training model for convolutional-graph neural networks

1. prepare_dataset_graph.py--First, you should prepare the molecular file mols.sdf from ChEMBL database like the example. Then you will get two files train.pickle, valid.pickle in datasets/ when you run the script for data preparation.

2. train_graph.py--The purpose of this code is to train the graph-convolutional neural network model for pka prediction, the parameter file of MolGpKa will save in models/. You need to train the model for acidic ioniable center and basic ioniable center separately with corresponding data.

Training model for AP-DNN

src/baseline/prepare_dataset_ap.py
src/baseline/train_ap.py

These scripts are designed to construct AP-DNN model which contain data preparation and model training.

Benchmark set for pka substitution effects

In order to test the substitution effects extensively, we created a benchmark set by performing matched molecular pair analysis on experimental pKa data sets collected by Baltruschat et al. The benchmark set contains 4322 data points.