Application of Deep Learning to Predict Human Oral Bioavailability of Pharmaceuticals DOI

Wei Lin,

Fang Ye, Peng Chen

et al.

Published: May 21, 2025

Abstract High failure rates in drug development are predominantly driven by suboptimal ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, with human oral bioavailability (HOB) serving as a critical determinant of therapeutic efficacy safety. Traditional HOB assessment methods, reliant on animal models clinical trials, face inherent limitations cost, scalability, reproducibility. To address these challenges, this study proposes deep learning framework integrating the directed message-passing neural network (D-MPNN) from Chemprop tool RDKit-derived molecular descriptors, enhancing predictive accuracy through hybrid representations atomic/bond-level graph features global physicochemical properties. Bayesian optimization automated hyperparameter tuning, while ensemble (20 models) ensured robustness for model development. The optimized achieved an AUC 0.8299 77.65% internal validation, outperforming existing tools 75 % external FDA-approved drugs. Interpretability analysis identified substructures correlated high HOB, providing actionable insights rational design. This work establishes novel method high-throughput screening candidates favorable bioavailability, highlighting potential to decode complex structure-property relationships pharmaceutical optimization.

Language: Английский

Application of Deep Learning to Predict Human Oral Bioavailability of Pharmaceuticals DOI

Wei Lin,

Fang Ye, Peng Chen

et al.

Published: May 21, 2025

Abstract High failure rates in drug development are predominantly driven by suboptimal ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, with human oral bioavailability (HOB) serving as a critical determinant of therapeutic efficacy safety. Traditional HOB assessment methods, reliant on animal models clinical trials, face inherent limitations cost, scalability, reproducibility. To address these challenges, this study proposes deep learning framework integrating the directed message-passing neural network (D-MPNN) from Chemprop tool RDKit-derived molecular descriptors, enhancing predictive accuracy through hybrid representations atomic/bond-level graph features global physicochemical properties. Bayesian optimization automated hyperparameter tuning, while ensemble (20 models) ensured robustness for model development. The optimized achieved an AUC 0.8299 77.65% internal validation, outperforming existing tools 75 % external FDA-approved drugs. Interpretability analysis identified substructures correlated high HOB, providing actionable insights rational design. This work establishes novel method high-throughput screening candidates favorable bioavailability, highlighting potential to decode complex structure-property relationships pharmaceutical optimization.

Language: Английский

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