HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction

TL;DR

HAC-Net is a novel deep learning architecture combining 3D CNNs and graph neural networks with attention mechanisms, achieving state-of-the-art accuracy in protein-ligand binding affinity prediction and demonstrating strong generalizability across diverse datasets.

Contribution

This work introduces HAC-Net, a hybrid attention-based neural network that integrates 3D CNNs and graph neural networks for improved binding affinity prediction.

Findings

Achieved state-of-the-art results on PDBbind v.2016 core set.

Demonstrated strong generalizability across various train-test splits.

Performed well on lower-quality data.

Abstract

Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting of a 3-dimensional convolutional neural network utilizing channel-wise attention and two graph convolutional networks utilizing attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based Convolutional Neural Network) obtains state-of-the-art results on the PDBbind v.2016 core set, the most widely recognized benchmark in the field. We extensively assess the generalizability of our model using multiple train-test splits, each of which maximizes differences between either protein structures, protein sequences, or ligand extended-connectivity fingerprints of complexes in the training and test sets. Furthermore, we perform 10-fold cross-validation with a similarity cutoff between SMILES strings of ligands in the training and test sets, and also evaluate the performance of HAC-Net on lower-quality data. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction. All of our software is available as open source at https://github.com/gregory-kyro/HAC-Net/, and the HACNet Python package is available through PyPI.