MF-SuP-pKa

Pytorch implementation of “MF-SuP-pKa: multi-fidelity modeling with subgraph pooling mechanism for pKa prediction”

• MF-SuP-pKa is a novel pka prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. Compared with Attentive FP, MF-SuP-pKa achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the DataWarrior acidic and basic sets, respectively.

Overview

• AttentiveFP/: the implementation of Attentive FP for pka prediction.
• Graph_pka/: the implementation of Graph-pKa.
• MF_SuP_pka/: the source codes of MF-SuP-pKa.
• data/: the pre-training data set, fine-tuning data set, and external test data sets used in MF-SuP-pKa.
• model/: the pre-trained model weights of MF-SuP-pKa.
• prediction/: the prediction results on SAMPL6, SAMPL7, and Novartis data sets by MF-SuP-pKa and other counterparts.

Requirements

• python 3.6
• rdkit 2021.09.4
• sklearn 0.24.2
• torch 1.10.1
• dgl 0.6.0

Usage

• Build graph data set
  python build_pka_graph_dataset.py --dataset pka_acidic_2750 --type acid
• Train the MF-SuP-pKa model
  • Train from scratch
    python MF_SuP_pka_model.py --task_name pka_acidic_2750 --type acid --k_hop 2 --stage before_transfer
  • Fine-tuning the pre-trained model
    python MF_SuP_pka_model.py --task_name pka_acidic_2750 --type acid --k_hop 2 --stage transfer --pretrain_aug

Reference

If you use this code, please cite the following paper:

Wu, J., Wan, Y., Wu, Z., Zhang, S., Cao, D., Hsieh, C. Y., & Hou, T. (2022). MF-SuP-pKa: multi-fidelity modeling with subgraph pooling mechanism for pKa prediction. Acta Pharmaceutica Sinica B.

Links: https://www.sciencedirect.com/science/article/pii/S2211383522004622