ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries скачать в хорошем качестве

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 molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). Professor James Zou (Stanford) and colleagues 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 This video demonstrates how to use ADMET-AI in your own projects using Google Colab and Python to create ADMET reports for your small molecules. Colab notebook: https://colab.research.google.com/dri... Please check out their open access paper and free code via their links: Bioinformatics . 2024 Jul 1;40(7):btae416. doi: 10.1093/bioinformatics/btae416. https://github.com/swansonk14/admet_ai