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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

Crossref DOI link: https://doi.org/10.1007/s10822-026-00854-x

Published Online: 2026-06-08

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Banerjee, Arkaprava https://orcid.org/0000-0001-8468-0784
Kumar, Vinay https://orcid.org/0000-0002-6809-7633
Das, Shubha https://orcid.org/0009-0004-1596-8900
Bhattacharyya, Prodipta https://orcid.org/0009-0000-2211-4795
Ojha, Probir Kumar https://orcid.org/0000-0003-4796-3915
Dasgupta, Indrasis https://orcid.org/0009-0006-3780-3466
Gayen, Shovanlal https://orcid.org/0000-0002-3367-578X
Chandra, Yogendra https://orcid.org/0009-0003-6344-168X
Roy, Partha Pratim https://orcid.org/0000-0003-2357-3234
Yang, Siyun https://orcid.org/0009-0007-9073-8437
Li, Lu https://orcid.org/0009-0003-1603-6825
Kar, Supratik https://orcid.org/0000-0002-9411-2091
Bhat-Ambure, Jyotsna https://orcid.org/0000-0002-1500-7040
Ambure, Pravin https://orcid.org/0000-0001-7244-7117
Roy, Kunal https://orcid.org/0000-0003-4486-8074
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Text and Data Mining valid from 2026-06-08

Version of Record valid from 2026-06-08
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Article History

Received: 25 December 2025

Accepted: 31 May 2026

First Online: 8 June 2026

Declarations

:

: The authors declare no conflict of interest.