Sergey Sosnin

Graduated from: Moscow State University

Research interests: chemoinformatics, deep learning

Thesis title: Exploration of chemical space with machine learning methods and molecular modeling

Publications:

• 3D Matters! 3D-RISM and 3d convolutional neural network for accurate bioaccumulation prediction / S. Sosnin, M. Misin, D. Palmer, M. Fedorov // Journal of Physics Condensed Matter. — 2018. — Vol. 30, no. 32. Q1 journal. (IF=2.7)

• A survey of multi-task learning methods in chemoinformatics / S. Sosnin, M. Vashurina, M. Withnall et al. // Molecular informatics. — 2019. — Vol. 37. Q2 journal. (IF=2.4)

• Comparative study of multitask toxicity modeling on a broad chemical space / S. Sosnin, D. Karlov, I. V. Tetko, M. V. Fedorov // Journal of Chemical Information and Modeling. 2019, 59, 3, 1062-1072 ,Q1 journal. (IF=3.8)

• Influence of descriptor implementation on compound ranking based on multi-parameter assessment / E. A. Sosnina, S.Sosnin, D. I. Osolodkin, E. V. Radchenko et al. // Journal of Chemical Information and Modeling. — 2018. — Vol. 58, no. 5. — P. 1083–1093. Q1 journal. (IF=3.8)

• Chemical space exploration guided by deep neural networks / D. S. Karlov, S. Sosnin, I. V. Tetko, M. V. Fedorov // RSC advances. — 2019. — no. 9. — P. 5151–5157. Q1 journal. (IF=3.0)

• Hydrogen/deuterium exchange aids compounds identification for LC-MC and MALDI imaging lipidomics / Y. I. Kostyukevich, G. Vladimirov, E. Stekolschikova, S. Sosnin et al. // Analytical Chemistry. — 2019, doi: 10.1021/acs.analchem.9b02461 Q1 journal. (IF=6.3)

Conferences:

• 22nd European Symposium on Quantitative Structure-Activity Relationships, Thessaloniki, Greece, 2018

• 257 American Chemical Society national meeting, Orlando, Florida, March 31- April 4, 2019