FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
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SCPT creates similarity-constrained preference triplets from scaffolds to train LLMs as conditional molecular editors that improve properties while keeping scaffolds intact.
AMORTIX uses group-normalized rewards in reinforcement learning to train an amortized Graph Transformer that optimizes constrained molecules in one forward pass and outperforms baselines on kinase inhibitor and prodrug design tasks.
A systematic method leveraging Weisfeiler-Leman coloring to mine class-discriminating motifs as proxy explanations, enabling the creation of the OpenGraphXAI benchmark suite from real-world datasets.
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.
citing papers explorer
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FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
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Scaffold-Conditioned Preference Triplets for Controllable Molecular Optimization with Large Language Models
SCPT creates similarity-constrained preference triplets from scaffolds to train LLMs as conditional molecular editors that improve properties while keeping scaffolds intact.
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Amortized Molecular Optimization via Group Relative Policy Optimization
AMORTIX uses group-normalized rewards in reinforcement learning to train an amortized Graph Transformer that optimizes constrained molecules in one forward pass and outperforms baselines on kinase inhibitor and prodrug design tasks.
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A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring
A systematic method leveraging Weisfeiler-Leman coloring to mine class-discriminating motifs as proxy explanations, enabling the creation of the OpenGraphXAI benchmark suite from real-world datasets.
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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.