ArticleFuture medicinal chemistry2025
Half-life prediction of central nervous system (CNS) small molecules in humans using gradient tree boosting.
Article in Future medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
aimsTo develop a machine learning (ML) model for early-stage prediction of human half-life of oral central nervous system (CNS) drugs and to establish a curated dataset, including key MATERIALS &
methodsHuman and rat half-life, plasma protein binding (PPB), and liver microsomal clearance (LM) data for 76 diverse CNS drugs and candidates were obtained from public sources or evaluated at WuXi AppTec. Gradient tree boosting (GTB) models were constructed using ChemAxon's Trainer Engine. Feature importance was assessed, and model performance was evaluated on an external validation set.
resultsThe best-performing model achieved 82.4% of predictions within two-fold of observed values, with a coefficient of determination (R
conclusionThis model demonstrates practical applications for early-stage prediction of human half-life and prioritization of CNS drug candidates. The curated dataset offers a valuable resource to enhance internal databases and advance more robust predictive models.
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