ADMET-vault: an interactive framework for real-time ADMET prediction and molecular optimization

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUC-ROC 0.87-0.99 for classification tasks

• Primary Outcome: ADMET prediction accuracy across multiple endpoints

• Nnt Or Sensitivity: Spearman correlation 0.44-0.66 for regression tasks; MAE 0.28-7.68 depending on endpoint

• Confidence Interval: Not explicitly reported

Clinical Application

High feasibility as computational tool requiring standard computing infrastructure Highly relevant for Australian pharmaceutical R&D, biotechnology companies, and academic drug discovery programs. Could support TGA submission preparation by improving early ADMET profiling Pharmaceutical researchers and medicinal chemists in early-stage drug discovery

Abstract

Drug discovery pipelines face persistent challenges, as candidates that show promising results in early laboratory studies are found ineffective in clinical trials-often because their absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles were not well understood early enough. In this study, an interactive machine-learning based platform, ADMET-Vault, is presented, which brings ADMET prediction directly into the molecular design process, allowing researchers to assess and optimize drug candidates during the molecule design phase itself. ADMET-Vault integrates multiple molecular representations including physicochemical descriptors, molecular fingerprints, and graph neural network embeddings. The platform evaluated 12 benchmark datasets from Therapeutics Data Commons using scaffold-based validation. ADMET-Vault demonstrated consistent predictive performance across multiple ADMET endpoints. Strong results were observed for clearance related properties, intestinal absorption, hepatotoxicity, and aqueous solubility. Moreover, regression showed mean absolute error value that ranged from 0.28 (Caco-2 permeability, expressed as log cm/s) to 7.68 (plasma protein binding rate, expressed as % bound), with Spearman's rank correlation coefficients between 0.44 and 0.66. Classification tasks showed area under the receiver operating characteristic curve values ranged between 0.87 and 0.99. ADMET-Vault's main contribution is not just in its predictive accuracy. It combines reliable machine learning models with a live molecular editor. This allows researchers to see how a structural change impacts drug-like properties instantly. This interactive design-predict-optimize loop fills a long-standing gap in early-stage drug discovery workflows.

References

1. 1.Vishwakarma, R., Lokhande, V., Punia, P., & Mishra, A. (2026). ADMET-vault: an interactive framework for real-time ADMET prediction and molecular optimization. Journal of Computer-Aided Molecular Design. https://doi.org/10.1093/nar/gkae236