ToolMol is an evolutionary agentic framework that pairs multi-objective genetic algorithms with LLM tool-calling to generate drug-like ligands with over 10% better predicted binding affinity and 35% better ABFE scores than prior methods.
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Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.
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ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
ToolMol is an evolutionary agentic framework that pairs multi-objective genetic algorithms with LLM tool-calling to generate drug-like ligands with over 10% better predicted binding affinity and 35% better ABFE scores than prior methods.
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Benchmarking open-source tools for in silico antiviral drug discovery
Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.