6 papers · 1 filter
QK-Wanda: Coupling Queries and Keys for Unstructured Pruning
Ivan Ilin, Peter Richtárik
Wanda (Sun et al., 2024) prunes large language models by scoring weights independently within each linear projection, although queries and keys interact through dot products. We in…
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…
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning
Ivan Ilin, Philip Zmushko, Peter Richtárik
Large language models (LLMs) remain expensive to fine-tune because full-parameter updates require substantial memory, compute, and per-task storage. We study whether saliency signa…
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…
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…