Protein-ligand structure and affinity prediction in CASP16 using a geometric deep learning ensemble and flow matching

Protein-ligand structure and affinity prediction in CASP16 using a geometric deep learning ensemble and flow matching | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL PROTEINS: Structure, Function, and Bioinformatics This is a preprint and has not been peer reviewed. Data may be preliminary. 29 January 2025 V1 Latest version Share on Protein-ligand structure and affinity prediction in CASP16 using a geometric deep learning ensemble and flow matching Authors : Alex Morehead , Jian Liu , Pawan Neupane , Nabin Giri , and Jianlin Cheng 0000-0003-0305-2853 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173812699.96369139/v1 Published Proteins: Structure, Function, and Bioinformatics Version of record Peer review timeline 995 views 497 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein-ligand structure (poses) produced by the latest deep learning methods to identify the best poses and how to accurately estimate the binding affinity between a protein target and a list of ligand candidates. Further, a blind benchmarking and assessment of protein-ligand structure and binding affinity prediction is necessary to ensure it generalizes well to new settings. Towards this end, we introduce MULTICOM_ligand, a deep learning-based protein-ligand structure and binding affinity prediction ensemble featuring structural consensus ranking for unsupervised pose ranking and a new deep generative flow matching model for joint structure and binding affinity prediction. Notably, MULTICOM_ligand ranked among the top-5 ligand prediction methods in both protein-ligand structure prediction and binding affinity prediction in the 16th Critical Assessment of Techniques for Structure Prediction (CASP16), demonstrating its efficacy and utility for real-world drug discovery efforts. The source code for MULTICOM_ligand is freely available on GitHub. Supplementary Material File (multicom_ligand_casp16_v2.pdf) Download 2.87 MB Information & Authors Information Version history V1 Version 1 29 January 2025 Peer review timeline Published Proteins: Structure, Function, and Bioinformatics Version of Record 8 Apr 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection PROTEINS: Structure, Function, and Bioinformatics Keywords binding affinity deep learning diffusion model drug discovery flow matching pose prediction protein-ligand structure Authors Affiliations Alex Morehead University of Missouri View all articles by this author Jian Liu University of Missouri View all articles by this author Pawan Neupane University of Missouri View all articles by this author Nabin Giri University of Missouri View all articles by this author Jianlin Cheng 0000-0003-0305-2853 [email protected] University of Missouri View all articles by this author Metrics & Citations Metrics Article Usage 995 views 497 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Alex Morehead, Jian Liu, Pawan Neupane, et al. Protein-ligand structure and affinity prediction in CASP16 using a geometric deep learning ensemble and flow matching. Authorea . 29 January 2025. 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