Visually Interpretable Models of Kinase Selectivity Related Features Derived from Field-Based Proteochemometrics. - Vigneshwari Subramanian; Peteris Prusis; Lars-Olof Pietilä; Henri Xhaard; Gerd Wohlfahrt (J. Chem. Inf. Model., 2013)

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks. - Ali Oskooei; Jannis Born; Matteo Manica; Vigneshwari Subramanian; Julio Sáez-Rodríguez; María Rodríguez Martínez (CoRR, 2018)

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders. - Matteo Manica; Ali Oskooei; Jannis Born; Vigneshwari Subramanian; Julio Sáez-Rodríguez; María Rodríguez Martínez (CoRR, 2019)

Multisolvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors. - Vigneshwari Subramanian; Ekaterina Ratkova; David Palmer 002; Ola Engkvist; Maxim V. Fedorov; Antonio Llinàs (J. Chem. Inf. Model., 2020)

Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images. - Andrés Martínez Mora; Vigneshwari Subramanian; Filip Miljkovic (J. Comput. Aided Mol. Des., 2022)

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. - Jonathan G. M. Conn; James W. Carter; Justin J. A. Conn; Vigneshwari Subramanian; Andrew Baxter; Ola Engkvist; Antonio Llinàs; Ekaterina Ratkova; Stephen D. Pickett; James L. McDonagh; David S. Palmer (J. Chem. Inf. Model., 2023)