V
Vladimir Chupakhin
Researcher at Janssen Pharmaceutica
Publications - 31
Citations - 651
Vladimir Chupakhin is an academic researcher from Janssen Pharmaceutica. The author has contributed to research in topics: Small molecule & GABAA receptor. The author has an hindex of 13, co-authored 31 publications receiving 483 citations. Previous affiliations of Vladimir Chupakhin include Russian Academy of Sciences & University of Strasbourg.
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Journal ArticleDOI
Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery
Jaak Simm,Günter Klambauer,Adam Arany,Marvin Steijaert,Jörg K. Wegner,Emmanuel Gustin,Vladimir Chupakhin,Yolanda T. Chong,Jorge Vialard,Peter Jacobus Johannes Antonius Buijnsters,Ingrid Velter,A. Vapirev,Shantanu Singh,Anne E. Carpenter,Roel Wuyts,Sepp Hochreiter,Yves Moreau,Hugo Ceulemans +17 more
TL;DR: It is hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes.
Journal ArticleDOI
ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics
Jiangming Sun,Nina Jeliazkova,Vladimir Chupakhin,Jose-Felipe Golib-Dzib,Ola Engkvist,Lars Carlsson,Jörg K. Wegner,Hugo Ceulemans,Ivan Georgiev,Vedrin Jeliazkov,Nikolay Kochev,Thomas J. Ashby,Hongming Chen +12 more
TL;DR: In this article, the authors compile a comprehensive chemogenomics dataset with over 70 million SAR data points from publicly available databases (PubChem and ChEMBL) including structure, target information and activity annotations.
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Predicting Ligand Binding Modes from Neural Networks Trained on Protein–Ligand Interaction Fingerprints
TL;DR: A novel approach to predict protein-ligand binding modes from the single two-dimensional structure of the ligand, which enable to efficiently rerank cross-docking poses and prioritize the best possible docking solutions.
Proceedings ArticleDOI
Macau: Scalable Bayesian factorization with high-dimensional side information using MCMC
Jaak Simm,Adam Arany,Pooya Zakeri,Tom Haber,Joerg Wegner,Vladimir Chupakhin,Hugo Ceulemans,Yves Moreau +7 more
TL;DR: This paper proposes a prior for the link matrix whose strength is proportional to the scale of latent variables, and derives an efficient sampler, with linear complexity in the number of non-zeros, O(Nnz), by leveraging Krylov subspace methods, such as block conjugate gradient, allowing to handle million-dimensional side information.
Journal ArticleDOI
Industry-scale application and evaluation of deep learning for drug target prediction
Noé Sturm,Andreas Mayr,Thanh Le Van,Vladimir Chupakhin,Hugo Ceulemans,Joerg Wegner,Jose-Felipe Golib-Dzib,Nina Jeliazkova,Yves Vandriessche,Stanislav Böhm,Vojtech Cima,Jan Martinovič,Nigel Greene,Tom Vander Aa,Thomas J. Ashby,Sepp Hochreiter,Ola Engkvist,Günter Klambauer,Hongming Chen +18 more
TL;DR: This is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.