# Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches

arXiv: https://arxiv.org/abs/2410.00709 · HTML: https://mlsanity.com/paper/2410.00709/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 169 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction](https://mlsanity.com/paper/2303.03543/) (2023)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity](https://mlsanity.com/paper/2107.10670/) (2021)
- [DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding](https://mlsanity.com/paper/2211.11214/) (2022)
- [How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities](https://mlsanity.com/paper/2409.11654/) (2024)
- [Learning Hierarchical Protein Representations via Complete 3D Graph Networks](https://mlsanity.com/paper/2207.12600/) (2022)
- [Multi-Scale Representation Learning on Proteins](https://mlsanity.com/paper/2204.02337/) (2022)
- [Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference](https://mlsanity.com/paper/2005.07704/) (2020)
- [PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions](https://mlsanity.com/paper/2008.12249/) (2020)
- [WideDTA: prediction of drug-target binding affinity](https://mlsanity.com/paper/1902.04166/) (2019)
- [DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/1912.00318/) (2019)
- [Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks](https://mlsanity.com/paper/2212.03447/) (2022)
- [Entropy-Reinforced Planning with Large Language Models for Drug Discovery](https://mlsanity.com/paper/2406.07025/) (2024)
- [Technical Report of HelixFold3 for Biomolecular Structure Prediction](https://mlsanity.com/paper/2408.16975/) (2024)
- [From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning](https://mlsanity.com/paper/2208.10230/) (2022)

## Built on by (most cited first, 3 of 3)

- [ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization](https://mlsanity.com/paper/2502.06891/) (2025)
- [Bidirectional Hierarchical Protein Multi-Modal Representation Learning](https://mlsanity.com/paper/2504.04770/) (2025)
- [ToxBench: A Binding Affinity Prediction Benchmark with AB-FEP-Calculated Labels for Human Estrogen Receptor Alpha](https://mlsanity.com/paper/2507.08966/) (2025)

## Abstract (arXiv)

Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein. Binding affinity, which characterizes the strength of biomolecular interactions, is essential for tackling diverse challenges in life sciences, including therapeutic design, protein engineering, enzyme optimization, and elucidating biological mechanisms. Much work has been devoted to predicting binding affinity over the past decades. Here, we review recent significant works, with a focus on methods, evaluation strategies, and benchmark datasets. We note growing use of both traditional machine learning and deep learning models for predicting binding affinity, accompanied by an increasing amount of data on proteins and small drug-like molecules. With improved predictive performance and the FDA's phasing out of animal testing, AI-driven in silico models, such as AI virtual cells (AIVCs), are poised to advance binding affinity prediction; reciprocally, progress in building binding affinity predictors can refine AIVCs. Future efforts in binding affinity prediction and AI-driven in silico models can enhance the simulation of temporal dynamics, cell-type specificity, and multi-omics integration to support more accurate and personalized outcomes.