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This paper presents CAi Copilot, an expert-oriented agent with three linked layers that turns molecular design intent into executable, traceable workflows, achieving the strongest performance across 45 tasks.
This paper proposes JoPMol, a jointly controlled precision molecular generative model that integrates gene expression profiles, molecular structure text, and chemical properties to generate personalized drug candidates, outperforming state-of-the-art methods.
Proposes two complementary approaches to incorporate predictive uncertainty into reinforcement learning for chemical language models, improving robustness and increasing true hit rate by 0.25 in de novo molecular design.
Introduces Constrained Flow Optimization (CFO), a framework for fine-tuning generative flow models to maximize rewards while satisfying constraints in molecular design, with theoretical guarantees and experimental validation.
EPFL researchers developed Synthegy, an AI framework that uses large language models to guide chemical retrosynthesis and reaction mechanism analysis through natural language instructions, significantly improving strategic planning for chemists.
ChemAmp introduces a tool amplification paradigm that dynamically coordinates specialized chemistry tools (UniMol2, Chemformer) as composable agents to enhance performance on molecular tasks. The framework outperforms chemistry-specialized models and reduces inference token costs by 94% compared to vanilla multi-agent systems.