Author

Mathieu Reymond

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#artificial intelligence Mar 2026

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. · arXiv.org · 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