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Author

Alex Zhavoronkov

5 papers indexed here

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Open access Sep 2026

Integration of proteomic aging clocks in a phase 2a clinical trial supports simultaneous geroprotective assessment.

Drugs for aging-related diseases may modulate aging itself, but standard clinical trial designs cannot detect such effects. Aging clocks could close this gap, but epigenetic models often yield inconsistent, hard-to-interpret results. In contrast, proteomic clocks, by tracking the immediate effectors of biological chang...

Alex Zhavoronkov, F. Galkin, Shan Chen et al. · 1 citation

MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery

Across essential drug discovery tasks - including molecular optimization, ADMET property prediction, retrosynthesis, drug-target activity prediction, and functional group reasoning - the resulting model achieves near specialist-level performance and, in the majority of settings, surpasses larger models, while remaining...

Maksim Kuznetsov, Z. Miftahutdinov, Shayakhmetov Rim et al. · 1 citation
Jul 2026

URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment

The URSA (Utilitarian RetroSynthesis Assessment) evaluation framework is introduced that provides the opportunity to benchmark the synthetic routes not only from a formal perspective, such as convergence to commercially available starting materials, but also from a chemical plausibility perspective, mimicking the way e...

B. Zagribelnyy, Ivan D. Ilin, N. Bondarev et al. · 0 citations
Jul 2026

Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints

A clear pattern is revealed in LLM spatial capabilities: while they still lag behind state-of-the-art approaches, they are promising and can handle multiple spatial constraints simultaneously, enabling scaling to heterogeneous setups.

Thomas MacDougall, Maksim Kuznetsov, Roman Schutski et al. · 1 citation
#artificial intelligence Preprint Aug 2026

Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis

This work introduces Top-K prompting as a robust training and inference paradigm to better capture diverse, plausible reaction predictions and establishes Top-K, plausibility-aware training as a practical new direction for robust future LLM-based synthesis planning.

B. Zagribelnyy, Ivan D. Ilin, N. Bondarev et al. · 1 citation

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