ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries

The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecul...

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Veröffentlicht in:Bioinformatics (Oxford, England) Jg. 40; H. 7
Hauptverfasser: Swanson, Kyle, Walther, Parker, Leitz, Jeremy, Mukherjee, Souhrid, Wu, Joseph C, Shivnaraine, Rabindra V, Zou, James
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 01.07.2024
Schlagworte:
Applications Note
Drug Discovery - methods
Machine Learning
Small Molecule Libraries - chemistry
Software
ISSN:1367-4803, 1367-4811, 1367-4811
Online-Zugang:Volltext
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Zusammenfassung:The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest public ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 h. The ADMET-AI platform is freely available both as a web server at admet.ai.greenstonebio.com and as an open-source Python package for local batch prediction at github.com/swansonk14/admet_ai (also archived on Zenodo at doi.org/10.5281/zenodo.10372930). All data and models are archived on Zenodo at doi.org/10.5281/zenodo.10372418.
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btae416