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Molecular Informatics|February 15, 2026
Read-Across Structure-Property Relationship-Based Superior Prediction of Fraction Unbound in Plasma from Chemical Structure: Interpretable Models with Minimum DescriptorsIndrasis Dasgupta, Samima Khatun, Shovanlal GayenBeilstein Journal of Nanotechnology|July 30, 2024
Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classificationIndrasis Dasgupta, Totan Das, Biplab Das, et al.Journal of Hazardous Materials|August 15, 2025
Machine learning-assisted comparative QSTR, i-QSTTR, qRASTR, and i-qRASTTR modelling for toxicity of Ionic liquids against three different bacteria S. aureus, E. coli, and P. aeruginosaIndrasis Dasgupta, Biplab Das, Sk Abdul Amin, et al.RSC Medicinal Chemistry|May 11, 2026
A new class of indole-based HDAC8 inhibitors as potential anti-lung cancer agents: <i>in silico</i> design, synthesis, biological assessment and binding interaction analysisSamima Khatun, Venkatesh Muthukumar, Ambati Himaja, et al.Molecular Diversity|June 13, 2024
Unveiling critical structural features for effective HDAC8 inhibition: a comprehensive study using quantitative read-across structure-activity relationship (q-RASAR) and pharmacophore modelingSamima Khatun, Indrasis Dasgupta, Rakibul Islam, et al.International Journal of Biological Macromolecules|November 28, 2024
Histone deacetylase 8 in focus: Decoding structural prerequisites for innovative epigenetic intervention beyond hydroxamatesSamima Khatun, Indrasis Dasgupta, Sourish Sen, et al.Molecular Diversity|May 23, 2026
An integrated machine learning and chemical space network approach for the design of potent epigenetic HDAC6 inhibitors for targeting neurological disordersIndrasis Dasgupta, Rupchand Pandit, Vijeta Jha, et al.Molecular Diversity|May 17, 2025
First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitorsRinki Prasad Bhagat, Jyotisha, Indrasis Dasgupta, et al.Journal of Computer-Aided Molecular Design|June 8, 2026
A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning modelsArkaprava Banerjee, Vinay Kumar, Shubha Das, et al.Pageof 1