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

Clicks: 199
ID: 278327
2023
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Abstract
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 Benchmark Group leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 hours.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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swanson2023admetaibiorxiv Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Swanson, Kyle;Walther, Parker;Leitz, Jeremy;Mukherjee, Souhrid;Wu, Joseph C;Shivnaraine, Rabindra V;Zou, James;
Journal bioRxiv : the preprint server for biology
Year 2023
DOI
2023.12.28.573531
URL
Keywords
Python open source image analysis live cell imaging ca2+ microdomains intracellular signaling shape normalization

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