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Articles

AI-enhanced adaptive virtual screening of large libraries for ligand discovery

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By Domiziana Cecchini, Akshat Kumar Nigam, Ming Tommy Tang, Joana C Reis, Matt Koop, Andrea Gottinger, Yao Wang, Abhilash Jayaraj, Ricarda Törner, Yehor S. Malets, Krishna Das Verified, Kelly Churion, Jongwan Kim, Nidhin Thomas, Yong Li, Hyuk-Soo Seo, Sirano Dhe-Paganon, Christopher Secker, Mohammad Haddadnia, Alexander Hasson, Abhishek Kumar, Roni Levin-Konigsberg, Eun-Bee Choi, Geoffrey I. Shapiro, Luke Sebastian, Chelsea Braithwaite, Puspalata Bashyal, Dmytro S. Radchenko, Aditya Kumar, Lei Yang, Henry Gabb, Amr Alhossary, Eric O’Neill, Gerhard Wagner, Alan Aspuru-Guzik, Yurii Moroz, Charalampos G. Kalodimos, Konstantin Fackeldey, John Schuetz, Andrea Mattevi, Haribabu Arthanari, Christoph Gorgulla, Callum Robert Nicoll, Minko Gehev, Minkai Li, Pierre-Yves Aquilanti
| Nature Verified
Abstract Ultralarge virtual screenings (ULVSs) evaluate billions of molecules for drug discovery but face cost, flexibility and scalability limits. We introduce AdaptiveFlow, an open-source platform that makes ULVSs more accessible, scalable and efficient and supports artificial intelligence (AI) and machine learning (ML) method development.
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