In the field of drug discovery, there has been a significant increase in potential drug candidates with the help of high-throughput docking and generative AI. However, a major challenge lies in identifying molecules with ideal drug properties. This is where ADMET-AI steps in.
ADMET-AI is an advanced machine-learning platform developed by researchers from Stanford University and Greenstone Biosciences. It is designed to rapidly and accurately forecast ADMET properties for extensive chemical libraries.
The platform utilizes a graph neural network called Chemprop-RDKit, which incorporates 200 physicochemical molecular features computed by RDKit. This unique combination allows ADMET-AI to predict a wide range of ADMET properties with exceptional accuracy.
ADMET-AI has been trained on 41 datasets from the Therapeutics Data Commons, outperforming other prediction tools in terms of speed and accuracy. It has also demonstrated its effectiveness in regression and classification tasks across these datasets.
One notable feature of ADMET-AI is its exceptional speed. The web server version of the platform is 45% faster than the next fastest ADMET web server. The local version of ADMET-AI provides high-throughput prediction capabilities, capable of processing one million molecules in just 3.1 hours.
In conclusion, ADMET-AI represents a major leap in drug discovery by offering a fast, precise, and adaptable platform for analyzing massive chemical libraries. Its accuracy in predicting ADMET features and ability to provide contextualized predictions based on a reference set of licensed drugs make it an invaluable tool for researchers and practitioners. With its speed, accuracy, and user-friendly interfaces, ADMET-AI meets the demand for effective screening tools in the face of increasing complexity in drug discovery campaigns and expanding chemical spaces.
For more information about ADMET-AI, you can check out the research paper, project, and Github. Stay updated with the latest news and developments by following us on Twitter.
The source of the article is from the blog newyorkpostgazette.com