In Silico ADMET: From Current Practices to Novel Profilers

Multitask learning is a promising strategy in computational drug discovery, potentially improving predictive performance and generalization over traditional single-task models. MTL has shown particular value in absorption, distribution, metabolism, elimination, and toxicity (ADMET) and potency predictions, which are key for drug design. Yet, many existing Web servers rely on the same uncurated, decade-old data sets, creating an illusion of diversity. This work critically reviews open-source ADMET Web services, revealing extensive data redundancy and limited curation across the field. We introduce OneADMET, a meticulously curated data set of 738,161 compounds with 1,119,719 measurements spanning 44 ADMET end points and 1 489 biological activities. We report a unified ChemProp-based MTL model capable of handling hundreds of continuous tasks simultaneously, which has practical advantages for model deployment and maintenance. Additionally, we observed that these MTL models match or surpass single-task models in predictive accuracy. This study highlights the utility of large-scale MTL for pharmacokinetics profiling and contributes practical tools and data sets for the community.