Researchers from Stanford and Greenstone Biosciences have developed ADMET-AI, a machine-learning platform utilizing generative AI and high-throughput docking to rapidly and accurately forecast drug properties. The platform’s integration of Chemprop-RDKit and 200 molecular features enables it to excel in predicting ADMET properties, offering exceptional speed and adaptability for drug discovery.
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ADMET-AI: Revolutionizing Drug Discovery with Fast and Accurate Predictions
The combination of enlarging chemical repositories and incorporating generative AI into drug discovery procedures has produced many promising drug candidates. Nevertheless, the real challenge lies in effectively identifying compounds with ideal druglike characteristics, specifically those related to absorption, distribution, metabolism, extraction, and toxicity (ADMET).
ADMET-AI: Fast and Accurate Predictions
A team of esteemed researchers from Stanford University and Greenstone Biosciences have introduced ADMET-AI, an advanced machine-learning platform designed to forecast ADMET properties for extensive chemical libraries rapidly and accurately. This platform uses a graph neural network called Chemprop-RDKit trained on 41 datasets from the Therapeutics Data Commons, allowing it to outperform other prediction tools in speed and accuracy. Its unique features include making predictions on batches of molecules and providing contextualized predictions based on a set of approved drugs. The platform has demonstrated its effectiveness across 41 TDC ADMET datasets, excelling in regression and classification tasks. Additionally, the local version of ADMET-AI enhances its practicality by providing high-throughput prediction capabilities, which can process one million molecules in just 3.1 hours.
Practical AI Solution
ADMET-AI is revolutionizing the field of drug discovery by providing a fast, precise, and adaptable platform for analyzing massive chemical libraries. Its accuracy in predicting ADMET features and its special ability to provide contextualized predictions against a reference set of licensed medications make it an indispensable tool for researchers and practitioners. Due to its speed, accuracy, and user-friendly interfaces, the platform represents a substantial leap in identifying drug candidates with optimum ADMET profiles for further development. The platform is available as a web-based service or a local tool, meeting the pressing demand for an effective screening tool in light of the growing complexity of drug discovery campaigns and the expansion of chemical spaces.
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