A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models

Arkaprava Banerjee; Vinay Kumar; Shubha Das; P.K. Bhattacharyya; Probir Kumar Ojha; Indrasis Dasgupta; Shovanlal Gayen; Yogendra Chandra; Partha Pratim Roy; Siyun Yang; Lu Li; Supratik Kar; Jyotsna Bhat-Ambure; Pravin Ambure; Kunal Roy

doi:10.1007/s10822-026-00854-x

A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models

Journal of Computer-Aided Molecular Design 2026

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Authors: Arkaprava Banerjee, Vinay Kumar, Shubha Das, P.K. Bhattacharyya, Probir Kumar Ojha, Indrasis Dasgupta, Shovanlal Gayen, Yogendra Chandra, Partha Pratim Roy, Siyun Yang, Lu Li, Supratik Kar

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Jyotsna Bhat-Ambure, Pravin Ambure, Kunal Roy

DOI: 10.1007/s10822-026-00854-x

Cited by: 1

References: 60

Fields of Study & Subjects

Computational Drug Discovery Methods Machine Learning in Materials Science Machine Learning and Data Classification

Publication details

Journal / source: Journal of Computer-Aided Molecular Design
Publisher: Springer Science and Business Media LLC
Volume: 40
Issue: 1
Publication date: 8 June 2026
Language: EN

References 60