A comprehensive evaluation of CHIMIYA-1, a proprietary autoselection foundation model developed by Covenant Biosciences, against the full TDC ADMET Group, showing particular strength on toxicity and physicochemical-property endpoints.
Artificial intelligence-driven ADMET prediction is becoming a vital tool in lowering attrition, speeding up candidate prioritization, and influencing the direction of rational drug development, despite persistent issues with data quality, regulatory acceptance, and synthetic viability.
Satyam Kumar Vishwash, Ram Babu Soni, Ratima Sood et al.· Current Computer - Aided Dru...· 0 citations
This work tested the key molecular descriptors capable of identifying antivirals, augmenting challenge data sets with curated public data, and building both classical machine learning and graph neural network (GNN) variants, providing both practical benchmarked modeling strategies for ADME and potency prediction and a...
Ida Titus, Ashok Palaniappan· Journal of Chemical Informat...· 0 citations
This work presents a model validation framework consisting of five recommendations that would enable the community to move beyond aggregate metrics toward understanding where and why molecular property prediction models fail, and connects evaluation choices to real-world applications and case studies encountered in pha...
Srijit Seal, Akshat Shirish Zalte, David Alencar Araripe et al.· bioRxiv· 0 citations
Toxicity remains a leading cause of late-stage attrition in drug development and is heavily influenced by a compound’s ADME (Absorption, Distribution, Metabolism, and Excretion) properties. The ability of a molecule to reach its target tissue, persist in circulation, or undergo bioactivation fundamentally shapes its...
Pranav Shah, C. Weber, Nivedita Kinatukara et al.· Scientific Reports· 0 citations
Overall, the results show that classifier design and optimisation account for a substantial portion of the improvement over MolTrans, while the contribution of cross-modal architectural components is optimisation-sensitive and dataset-dependent.
Hao Pang, Fiseha B. Tesema, Tian-Xiang Cui et al.· BMC Bioinformatics· 0 citations
Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical stages. As a result, accurate early prediction of chemical toxicity is essential to reduce downstream costs and improve compound prioritization. In this context, graph dee...
Noel Suarez-Barro, M. Lama, J. C. Vidal· 0 citations
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