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.
Efficient Evolutionary Search Over Chemical Space with Large Language Models
5 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
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representative citing papers
ParetoPilot uses Infer-Perturb-Guide inside reverse diffusion to push samples to the Pareto front without surrogates, ranking best among 16 methods on 51 offline MOO tasks.
General-purpose LLMs recover 96% of low-energy Elpasolites via iterative in-context learning, surpassing task-specific models on an established benchmark.
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.
SmileyLlama is an LLM transformed via SFT and DPO to generate valid novel drug-like molecules with user-specified properties and optimized 3D conformations for high binding affinity.
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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ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion
ParetoPilot uses Infer-Perturb-Guide inside reverse diffusion to push samples to the Pareto front without surrogates, ranking best among 16 methods on 51 offline MOO tasks.
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General-purpose LLMs as Constrained Crystal Composition Generators
General-purpose LLMs recover 96% of low-energy Elpasolites via iterative in-context learning, surpassing task-specific models on an established benchmark.
-
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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SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration
SmileyLlama is an LLM transformed via SFT and DPO to generate valid novel drug-like molecules with user-specified properties and optimized 3D conformations for high binding affinity.