Assessing the state of the art in ADMET prediction: Lessons from the OpenADMET-ExpansionRx community blind challenge
Year: 2026
Type: preprint
Abstract: Computational blind challenges are essential for advancing drug discovery, especially in research areas with limited public data, such as absorption, distribution, metabolism, excretion, toxicity (ADMET) prediction. In this paper, we present the results of our latest community blind challenge, organized in collaboration with Expansion Therapeutics (ExpansionRx). Participants were tasked with predicting nine distinct ADMET endpoints routinely evaluated during lead optimization of compounds in drug discovery programs. Following unprecedented engagement from the global community, this challenge provided valuable insights into the state of the art in machine learning for ADMET modeling. Evaluating submissions against a realistic, challenging dataset from active ExpansionRx drug discovery programs revealed that message-passing neural networks and chemical foundation models dominated among the top performers. Furthermore, the results highlighted the power of proprietary industry datasets and a critical need to optimize predictive workflows for different endpoints independently via multitasking and task-affinity grouping. We also demonstrate how hosting our open-source blind challenge infrastructure on a flexible platform like Hugging Face was pivotal for driving community engagement and visibility. Ultimately, blind challenges supported by robust infrastructure, widespread dissemination, and rigorous post-challenge analysis are fundamental to maximizing the utility of public data releases. We anticipate that these community benchmarks will continue to drive the advancement of ADMET models, enabling their deployment in both early- and late-stage drug discovery.
Source: ChemRxiv
Authors: Maria A. Castellanos, Hugo MacDermott-Opeskin, Jon Ainsley, Jonathan A. Swain, Sean Colby, Devany West, Georgia Channing, Andrew Good, Elyse Bourque, Lakshminarayana Vogeti, Renato Skerlj, Tiansheng Wang, Mark Ledeboer, David Alencar Araripe, Rafal Bachorz, Amitesh Badkul, Maximilian Beckers, Jérémy Besnard, Olivier Beyens, Maicol Bissaro, Davide Boldini, Laetitia Breuil, Jackson W Burns, Lianjin Cai, Ruel Cedeno, Joanna Ceklarz, Xin Chen, Alan C. Cheng, Vishnu Vardhan Chundu, Vladimir Chupakhin, Thomas Coudrat, Simon Crouzet, Daniel Crusius, Wim Dehaen, Valeriia Fil, Robert Fraczkiewicz, Jozef Fülöp, Enrico Gandini, David Giganti, Anthony Gitter, Alec Glisman, Pawel Gniewek, Ryan Greenhalgh, Malte Grieswelle, Oleg Gromov, Sudhir Gupta, Leonard F. Haasbroek, Calum Hand, Xibing He, Dimitrios Iliadis, Abdul Haleem Mohamed Iliiyas, Eachan Johnson, Kalen Josifovski, Shivananda Kandagalla, Lukas Kerti, Oliver Koch, Vaishnavi Kodamala, Stephanie Labouille, Shaolong Lin, Dmitriy Makarov, Tatiana Malygina, James L. McDonagh, Janosch Menke, Linqing Mo, Son Ngo, Ashok Palaniappan, Ferruccio Palazzesi, Csaba Petre, Long-Hung Pham, Will R Pitt, Gopi Krishna Ponnamudi, Suryavedha Pradhan, Eleonora Proia, John Proudfoot, Fabian Rauscher, Jacob Remington, Nicholas Rosa, Tamilazhagan S, Piyush Sawner, Lars L Schaaf, Femi Segun, Dolly Sharma, Harshit Singh, Rajeev Kumar Singh, Ramesh Sistla, Ryan Snyder, Thibaud Southiratn, Satya Pratik Srivastava, Chris Taylor, Igor Tetko, Elisha Gabrielle V. Tiu, Srajal Tiwari, Hunzallah Usmani, Jason Y. Wang, Peng Wang, Shihang Wang, Azmine Toushik Wasi, Brian Wylie, Lei Xie, Yuna Yan, Massa Zahdeh, Shuo Zhang, Donghai Zhao, Mingning (Nancy) Zhu, Martin Šícho, W. Patrick Walters
Institutions: Open Molecular Software Foundation, Hugging Face, University of Groningen, Simulations Plus (United States), The Graduate Center, CUNY, City University of New York, Volgograd Institute of Business, Aptuit (Italy), Evotec (United States), Merck KGaA, Darmstadt (Germany), Merck Healthcare KGaA, Darmstadt (Germany), Massachusetts Institute of Technology, University of Pittsburgh, Merck & Co., Inc., Rahway, NJ, USA (United States), Shiv Nadar University, Commonwealth Scientific and Industrial Research Organisation, École Polytechnique Fédérale de Lausanne, Schrodinger (United States), University of Wisconsin–Madison, Morgridge Institute for Research, University of Münster, Lomonosov Moscow State University, apheris AI GmbH (Germany), The Francis Crick Institute, University College London, Comenius University Bratislava, University of Chemistry and Technology, Prague, Evotec (United Kingdom), Macao Polytechnic University, Russian Academy of Sciences, Institute of Solution Chemistry, Helmholtz Institute for Pharmaceutical Research Saarland, Michigan State University, SASTRA University, Imperial College London, University of Cambridge, University of Warwick, Shiv Nadar Foundation, Rutgers, The State University of New Jersey, Sygnature Discovery (Canada), OpenEye (Sweden), Northeastern University, Al-Quds University, Hunter College, Zhejiang University, Stanford University
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7214098325