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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A single decoder-only model generates prompt-conditioned retrosynthetic routes and shows measurable gains on depth and required-leaf constraints in the RetroCast/PaRoutes benchmarks while releasing its code.
Multigrid training accelerates convergence and improves generalization for receptor-conditioned 3D ligand generation by transferring parameters from coarse to fine graph and voxel resolutions.
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.
MORetro* applies multi-objective A* search with weighted scalarization and BO-informed sampling to produce Pareto fronts of retrosynthetic routes with optimality guarantees when the single-step model is admissible.
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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.