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Journal of Chemical Information and Modeling|August 21, 2023
Meta-GTM: Visualization and Analysis of the Chemical Library SpaceRegina Pikalyova, Yuliana Zabolotna, Dragos Horvath, et al.Journal of Chemical Information and Modeling|June 27, 2023
Chemical Library Space: Definition and DNA-Encoded Library Comparison Study CaseRegina Pikalyova, Yuliana Zabolotna, Dragos Horvath, et al.Journal of Chemical Information and Modeling|October 30, 2023
Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic CatalystsDmitry Zankov, Timur Madzhidov, Pavel Polishchuk, et al.Journal of Computer-Aided Molecular Design|February 11, 2019
Multi-task generative topographic mapping in virtual screeningArkadii Lin, Dragos Horvath, Gilles Marcou, et al.Molecules (Basel, Switzerland)|June 10, 2023
Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson's Catalyst CaseRuben Staub, Philippe Gantzer, Yu Harabuchi, et al.Journal of Computer-Aided Molecular Design|February 15, 2011
Local neighborhood behavior in a combinatorial library contextDragos Horvath, Christian Koch, Gisbert Schneider, et al.Journal of Chemical Theory and Computation|December 26, 2025
An Accurate and Efficient Reaction Path Search with Iteratively Trained Neural Network Potential: Answering the Passerini Mechanism ControversyRuben Staub, Yu Harabuchi, Carine Seraphim, et al.Chemical Science|September 12, 2025
Predicting reaction conditions: a data-driven perspectiveMatthew Ball, Dragos Horvath, Thierry Kogej, et al.Nature Reviews. Drug Discovery|December 8, 2023
Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSARAlexander Tropsha, Olexandr Isayev, Alexandre Varnek, et al.Journal of Computer-Aided Molecular Design|August 14, 2019
Diversifying chemical libraries with generative topographic mappingArkadii Lin, Bernd Beck, Dragos Horvath, et al.Pageof 16