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Sosnin, S 2025, 'Chemical space visual navigation in the era of deep learning and Big Data', Drug Discovery Today, vol. 30, no. 7, 104392. https://doi.org/10.1016/j.drudis.2025.104392

Sosnin, S 2024, 'MolCompass: Multi-tool for the Navigation in Chemical Space and Visual Validation of QSAR/QSPR Models', Journal of Cheminformatics, vol. 16, no. 1, 98. https://doi.org/10.1186/s13321-024-00888-z

Granulo, N, Sosnin, S, Digles, D & Ecker, GF 2024, 'The macrocycle inhibitor landscape of SLC‐transporter', Molecular Informatics, vol. 43, no. 5, e202300287. https://doi.org/10.1002/minf.202300287

Huang, J, Osthushenrich, T, MacNamara, A, Mälarstig, A, Brocchetti, S, Bradberry, S, Scarabottolo, L, Ferrada, E, Sosnin, S, Digles, D, Superti-Furga, G & Ecker, GF 2024, 'ProteoMutaMetrics: Machine Learning Approaches for Solute Carrier Family 6 Mutation Pathogenicity Prediction', Rsc advances, vol. 14, no. 19, 00748, pp. 13083-13094. https://doi.org/10.1039/d4ra00748d

Smajić, A, Rami, I, Sosnin, S & Ecker, GF 2023, 'Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets', Chemical Research in Toxicology, vol. 36, no. 8, pp. 1300-1312. https://doi.org/10.1021/acs.chemrestox.3c00042

Sosnina, E, Sosnin, S & Fedorov, M 2023, 'Improvement of multi‑task learning by data enrichment: application for drug discovery', Journal of Computer-Aided Molecular Design, vol. 37, no. 4, pp. 183-200. https://doi.org/10.1007/s10822-023-00500-w