CACM improves language-based drug discovery agents by 36.4% via protocol auditing, a grounded diagnostician, and compressed static/dynamic/corrective memory channels that localize failures and bias corrections.
Quantifying the Chemical Beauty of Drugs
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ToolMol integrates evolutionary algorithms with agentic LLMs and precise RDKit tools to optimize multi-objective drug properties, yielding ligands with over 10% better predicted binding affinity and 35% gains in absolute binding free energy on three protein targets.
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
citing papers explorer
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Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents
CACM improves language-based drug discovery agents by 36.4% via protocol auditing, a grounded diagnostician, and compressed static/dynamic/corrective memory channels that localize failures and bias corrections.
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ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
ToolMol integrates evolutionary algorithms with agentic LLMs and precise RDKit tools to optimize multi-objective drug properties, yielding ligands with over 10% better predicted binding affinity and 35% gains in absolute binding free energy on three protein targets.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.