Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategies. - Andrei Dmitrenko; Mauro M. Masiero; Nicola Zamboni (MIDL, 2022)

Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data. - Andrei Dmitrenko; Mauro M. Masiero; Nicola Zamboni (MIDL, 2022)

Artificial Intelligence for Long-Term Reproducible High-Throughput Untargeted Metabolomics. - Andrei Dmitrenko (2022)

Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data. - Andrei Dmitrenko; Michelle Reid; Nicola Zamboni (Bioinform., 2023)

Unveiling the Potential of AI for Nanomaterial Morphology Prediction. - Ivan Dubrovsky; Andrei Dmitrenko; Aleksei Dmitrenko; Nikita Serov; Vladimir Vinogradov (ICML, 2024)

Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability. - Nina Gubina; Andrei Dmitrenko; Gleb V. Solovev; Lyubov Yamshchikova; Oleg Petrov; Ivan Lebedev; Nikita Serov; Grigorii Kirgizov; Nikolay O. Nikitin; Vladimir Vinogradov (NeurIPS, 2024)

Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery. - Maria Zavadskaya; Anastasia Orlova; Andrei Dmitrenko; Vladimir Vinogradov (J. Cheminformatics, 2025)