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.
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.
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.
Chemically meaningful steering for properties like cLogP and TPSA emerges in entangled Transformer-VAE latent spaces only after controlling for SELFIES representation confounds through residualization and decoded traversals.
GEMS is an interactive system that lets domain experts steer evolutionary molecule design for sustainable chemicals by modifying scoring and populations in a visual interface.
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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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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Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces
Chemically meaningful steering for properties like cLogP and TPSA emerges in entangled Transformer-VAE latent spaces only after controlling for SELFIES representation confounds through residualization and decoded traversals.
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GEMS -- Guided Evolutionary Molecule Design for Sustainable Chemicals
GEMS is an interactive system that lets domain experts steer evolutionary molecule design for sustainable chemicals by modifying scoring and populations in a visual interface.