37 citations · 72 across the 15 of their papers we have counts for
16 papers
Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis
Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev +5
Single-step retrosynthesis is a central component of computer-aided synthesis planning, yet its intrinsically one-to-many nature is poorly captured by single-answer evaluation and…
Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints
Thomas MacDougall, Maksim Kuznetsov, Roman Schutski +5
Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules. While di…
Remembering George Smoot
John Berliner, Tom Broadhurst, Martin Bucher +31
George Smoot (1945-2025) was a major figure in observational cosmology, particularly through his role in the precision measurement of the CMB dipole and the discovery of the primor…
URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev +7
Synthesis planning aiming to find pathways of reactions for a target molecule is one of the most important and challenging tasks in drug discovery. Recent progress has produced bot…
Control Laws in Aging and Longevity: A Control Theory of Aging for Gerotherapeutic Drug Discovery
Alex Zhavoronkov, Bud Mishra
Existing aging theories describe what changes with age but do not prescribe how to intervene. We propose a control-theoretic framework that is not merely descriptive but prescripti…
MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery
Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov +17
General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks…
When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs
Bogdan Zagribelnyy, Ivan Ilin, Maksim Kuznetsov +10
Recent progress has expanded the use of large language models (LLMs) in drug discovery, including synthesis planning. However, objective evaluation of retrosynthesis performance re…
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Artem Zholus, Maksim Kuznetsov, Roman Schutski +4
Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding of the complex physical interactions bet…
Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS
Mohammad Ghazi Vakili, Christoph Gorgulla, AkshatKumar Nigam +19
The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged novel computational techn…
nach0: Multimodal Natural and Chemical Languages Foundation Model
Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina +8
Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creat…
AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor
Feng Ren, Xiao Ding, Min Zheng +19
The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI)…
Chemistry42: An AI-based platform for de novo molecular design
Yan A. Ivanenkov, Alex Zhebrak, Dmitry Bezrukov +7
Chemistry42 is a software platform for de novo small molecule design that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methods. Che…
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling +13
Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with simila…
Evaluating race and sex diversity in the world's largest companies using deep neural networks
Konstantin Chekanov, Polina Mamoshina, Roman V. Yampolskiy +3
Diversity is one of the fundamental properties for the survival of species, populations, and organizations. Recent advances in deep learning allow for the rapid and automatic asses…
GrantMed: a new, international system for tracking grants and funding trends in the life sciences
Yuri Nikolsky, Roman Gurinovich, Oleg Kuryan +5
Despite the success of PubMed and other search engines in managing the massive volume of biomedical literature and the retrieval of individual publications, grant-related data rema…
Models of Innate Neural Attractors and Their Applications for Neural Information Processing
Ksenia P. Solovyeva, Iakov M. Karandashev, Alex Zhavoronkov +1
In this work we reveal and explore a new class of attractor neural networks, based on inborn connections provided by model molecular markers, the molecular marker based attractor n…