Hugo MacDermott-Opeskin, Jenke Scheen, Cas Wognum, Joshua T. Horton, Devany West, Alexander Matthew Payne, Maria A. Castellanos, Sean Colby, Ed Griffen, David L. Cousins, Jessica Stacey, Lauren Reid, Jasmin Cara Aschenbrenner, Daren Fearon, Blake Balcomb, Peter Marples, Charles W. E. Tomlinson, Ryan Lithgo, Andre S. Godoy, Max Winokan, Haim Barr, Noa Lahav, Michael Lavi, Shirley Duberstein, Galit Cohen, Gwendolyn Fate, Bruce A. Lefker, Ralph P. Robinson, Tamás Szommer, Nick Lynch, David D. L. Minh, Van Ngoc Thuy La, Lulu Kang, Kate Huddleston, Ryan Renslow, Mallory R. Tollefson, W. Patrick Walters, Cynthia Xu, Jonny Hsu, Julien St-Laurent, Honore Etsmoberg, Lu Zhu, Andrew Quirke, Mohamed Iliyas Abdul Haleem, Irfan Alibay, Gunjan Baid, Benjamin Birnbaum, Kevin P. Bishop, Hugo Bohorquez, Ashmita Bose, C. J. Brown, Jackson Burns, Lianjin Cai, Ruel Cedeno, Stephane de Cesco, Vladimir Chupakhin, Finlay Clark, Daniel J. Cole, Carles Corbi-Verge, Muhammad Danial, Alec Davi, Wim Dehaen, Niklas Piet Doering, Alexis Dougha, Marie-Pierre Dréanic, Bryce Eakin, Anatol Ehrlich, Rokas Elijošius, Jozef Fülöp, Anthony Gitter, Kenneth Goossens, Yaowen Gu, Teresa Head‐Gordon, Laurent Hoffer, Johan Hofmans, Ellena Jiang, Benjamin Kaminow, Sina Khosravi, Asma Feriel Khoualdi, Eelke B. Lenselink, Zhirong Liu, Yue Liu, Sijie Liu, Yizhou Ma, Patrick Maher, Imke Mayer, Oscar Méndez‐Lucio, Antonia S. J. S. Mey, Julien Michel, Floriane Montanari, Taoyu Niu, Ryusei Ogino, Ashok Palaniappan, Xiaolin Pan, Auro Patnaik, Long-Hung Dinh Pham, Luis Pinto, Justin Clement Purnomo, Alex Rich, Lars L. Schaaf
Computational blind challenges offer critical, unbiased opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the past decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform Discovery Consortium's pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay end points, using previously undisclosed comprehensive experimental drug discovery data sets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top-performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.