Vigneshwari Subramanian

AstraZeneca (Australia) — Australia

Computer Science · Physical Sciences

10h-index624citations32works5.52yr avg
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Research Topics

Computational Drug Discovery Methods(20), Machine Learning in Materials Science(10), Bioinformatics and Genomic Networks(8), Cell Image Analysis Techniques(5), Advanced Proteomics Techniques and Applications(3)

Publications32 total

Prediction of Permeability and Efflux Using Multitask Learning
ACS Omega·2025Open Access
PKSmart: an open-source computational model to predict intravenous pharmacokinetics of small molecules
Journal of Cheminformatics·2025· 4 citedOpen Access
PKPD modeling and in silico models
Elsevier eBooks·2025
Perspectives on the use of machine learning for ADME prediction at AstraZeneca
Xenobiotica·2024· 10 cited
Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity
Cell Biology and Toxicology·2024· 4 citedOpen Access
PKSmart: An Open-Source Computational Model to Predict <i>in vivo</i> Pharmacokinetics of Small Molecules
2024· 14 citedOpen Access
Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
Journal of Chemical Information and Modeling·2023· 23 citedOpen Access
Interpretation of multi-task clearance models from molecular images supported by experimental design
Artificial Intelligence in the Life Sciences·2022· 5 citedOpen Access
Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
2022Open Access
Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images
Journal of Computer-Aided Molecular Design·2022· 10 cited
Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images
Research Square (Research Square)·2022Open Access
Patient-specific Boolean models of signalling networks guide personalised treatments
eLife·2022· 94 citedOpen Access
Author response: Patient-specific Boolean models of signalling networks guide personalised treatments
2022Open Access
Patient-specific Boolean models of signaling networks guide personalized treatments
2021· 4 citedOpen Access
Multisolvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors
Journal of Chemical Information and Modeling·2020· 16 cited
Multi-Solvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors
2020· 2 citedOpen Access
Multi-Solvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors
2020· 3 citedOpen Access
Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
Molecular Pharmaceutics·2019· 145 citedOpen Access
Signatures of cell death and proliferation in perturbation transcriptomics data—from confounding factor to effective prediction
Nucleic Acids Research·2019· 70 citedOpen Access
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Frequent Co-authors

Julio Sáez-Rodríguez(9), Bence Szalai(7), Ola Engkvist(6), Antonio Llinàs(6), Ekaterina L. Ratkova(6), Luis Tobalina(5), Róbert Alföldi(5), László G. Puskás(5), David Scott Palmer(5), Filip Miljković(5), Gerd Wohlfahrt(4), Peteris Prūsis(4), Andreas Bender(4), Arnau Montagud(3), Jonas Béal(3), Pauline Traynard(3), Alfonso Valencia(3), Emmanuel Barillot(3), Laurence Calzone(3), Henri Xhaard(3)