# PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions

arXiv: https://arxiv.org/abs/2008.12249 · HTML: https://mlsanity.com/paper/2008.12249/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2020.

## Built on by (most cited first, 30 of 31)

- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [Structure-based drug design with geometric deep learning](https://mlsanity.com/paper/2210.11250/) (2022)
- [Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model](https://mlsanity.com/paper/2211.10590/) (2022)
- [Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation](https://mlsanity.com/paper/2301.10814/) (2023)
- [Protein-ligand binding representation learning from fine-grained interactions](https://mlsanity.com/paper/2311.16160/) (2023)
- [SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction](https://mlsanity.com/paper/2206.07015/) (2022)
- [Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction](https://mlsanity.com/paper/2306.04886/) (2023)
- [GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising](https://mlsanity.com/paper/2304.03724/) (2023)
- [A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2405.14545/) (2024)
- [Learning Physics-Consistent Particle Interactions](https://mlsanity.com/paper/2202.00299/) (2022)
- [Associative Learning Mechanism for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2205.15364/) (2022)
- [PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling](https://mlsanity.com/paper/2310.00681/) (2023)
- [Improved prediction of ligand-protein binding affinities by meta-modeling](https://mlsanity.com/paper/2310.03946/) (2023)
- [Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches](https://mlsanity.com/paper/2410.00709/) (2024)
- [Advances in Protein Representation Learning: Methods, Applications, and Future Directions](https://mlsanity.com/paper/2503.16659/) (2025)
- [Geometric Deep Learning for Structure-Based Drug Design: A Survey](https://mlsanity.com/paper/2306.11768/) (2023)
- [PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps](https://mlsanity.com/paper/2310.04017/) (2023)
- [MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation](https://mlsanity.com/paper/2412.07778/) (2024)
- [High Performance of Gradient Boosting in Binding Affinity Prediction](https://mlsanity.com/paper/2205.07023/) (2022)
- [QuickBind: A Light-Weight And Interpretable Molecular Docking Model](https://mlsanity.com/paper/2410.16474/) (2024)
- [Preferential Multi-Objective Bayesian Optimization for Drug Discovery](https://mlsanity.com/paper/2503.16841/) (2025)
- [A comprehensive analysis of PINNs: Variants, Applications, and Challenges](https://mlsanity.com/paper/2505.22761/) (2025)
- [ToxBench: A Binding Affinity Prediction Benchmark with AB-FEP-Calculated Labels for Human Estrogen Receptor Alpha](https://mlsanity.com/paper/2507.08966/) (2025)
- [Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery](https://mlsanity.com/paper/2508.01799/) (2025)
- [DiffDTM: A conditional structure-free framework for bioactive molecules generation targeted for dual proteins](https://mlsanity.com/paper/2306.13957/) (2023)
- [DualBind: A Dual-Loss Framework for Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2406.07770/) (2024)
- [SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction](https://mlsanity.com/paper/2407.11057/) (2024)
- [Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity](https://mlsanity.com/paper/2502.07293/) (2025)
- [BioLM-Score: Language-Prior Conditioned Probabilistic Geometric Potentials for Protein-Ligand Scoring](https://mlsanity.com/paper/2602.18476/) (2026)

## Abstract (arXiv)

Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in-silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom-atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein-ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interpretation of predicted affinities by visualizing the contribution of ligand substructures, providing insights for further ligand optimization.