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V. Aladinskiy

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Aug 2026

Discovery of Highly Potent and Selective, Orally Bioavailable BTK-Targeting PROTACs Featuring Novel CRBN-Binding Warheads.

Drugs targeting Bruton's tyrosine kinase (BTK) are recognized as key tools in hematological oncology. The clinical efficacy of BTK inhibitors is limited, however, by the emerging resistance driven by BTK mutations and the severe toxicity attributed to off-target kinase inhibition. PROteolysis TArgeting Chimeras (PROTAC...

A. Mantsyzov, Pei Zhao, Chris Kruse et al. · 0 citations

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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