A new paradigm for applying deep learning to protein–ligand interaction prediction

Zechen Wang, Sheng Wang, Yangyang Li, Jingjing Guo, Yanjie Wei, Yuguang Mu, Liangzhen Zheng, Weifeng Li

Briefings in Bioinformatics 2024 article cited by 40

Source Open-access PDF More in Biology & Life Sciences

importance 5/10 ai assisted Computational Drug Discovery Methods drug discovery stage: research

Problem

Predicting how small molecules dock into protein targets and how strongly they bind is slow and error-prone in drug design.

Why it matters

Accurate binding prediction reduces the cost and time of finding new drug candidates.

What the AI did

IGModel, a deep learning model, uses geometric information of protein-ligand complexes to predict docking pose RMSD and binding strength (pKd) in one framework.

Results

State-of-the-art accuracy on CASF-2016, PDBbind-CrossDocked-Core and DISCO docking test sets; no numbers stated in abstract.

Impact

Evidence supports benchmark-leading performance only; no real-world drug-discovery adoption stated.

Record

Venue	Briefings in Bioinformatics
Published	2024
Work type	article
Authors	Zechen Wang, Sheng Wang, Yangyang Li, Jingjing Guo, Yanjie Wei, Yuguang Mu, Liangzhen Zheng, Weifeng Li
Citations	40 (OpenAlex, at import time)
References	90
Model or method	IGModel
Organization	not stated in source
Primary source	https://doi.org/10.1093/bib/bbae145
Open access	gold
Keywords	protein-ligand, docking, deep learning, binding affinity, pKd, drug design

Importance rating

5 / 10

real but narrower advance

Binding prediction matters to drug design broadly, but abstract only supports benchmark-leading accuracy; no real-world drug-discovery impact stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

More in Biology & Life Sciences

#	Paper	Year	Citations	Importance
1	AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models Mihály Váradi, Stephen Anyango, Mandar Deshpande, Sreenath Sasidharan...	2021	7,782	9
2	Evolutionary-scale prediction of atomic-level protein structure with a language model Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting L...	2023	5,455	9
3	Accurate prediction of protein structures and interactions using a three-track neural network Minkyung Baek, Frank DiMaio, Ivan V. Anishchenko, Justas Dauparas, Ser...	2021	5,468	9
4	De novo design of protein structure and function with RFdiffusion Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Tripp...	2023	2,294	9
5	Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins Rebecca Buller, Jiřı́ Damborský, Donald Hilvert, Uwe T. Bornscheuer	2024	40	9