Public articles linked to the same research event.
Journal of Chemical Information and Modeling This work introduces PockLigGPT, a GPT-based framework for ligand generation conditioned on the amino acid sequence of a protein pocket, trained in four stages (large-scale ZINC20 chemical pretraining, ChEMBL bioactivity-oriented adaptation, pocket-sequence-conditioned fine-tuning, and pocket-specific docking-guided reinforcement learning with AutoDock Vina-based rewards), achieving competitive docking-oriented performance under a standardized evaluation protocol while maintaining chemical plausibility and Lipinski-based drug-likeness, with docking studies on Alzheimer's disease-associated targets and token-level analyses supporting its utility for de novo drug design.
This work introduces PockLigGPT, a GPT-based framework for ligand generation conditioned on the amino acid sequence of a protein pocket, trained in four stages (large-scale ZINC20 chemical pretraining, ChEMBL bioactivity-oriented adaptation, pocket-sequence-conditioned fine-tuning, and pocket-specific docking-guided reinforcement learning with AutoDock Vina-based rewards), achieving competitive docking-oriented performance under a standardized evaluation protocol while maintaining chemical plausibility and Lipinski-based drug-likeness, with docking studies on Alzheimer's disease-associated targets and token-level analyses supporting its utility for de novo drug design.
This work introduces PockLigGPT, a GPT-based framework for ligand generation conditioned on the amino acid sequence of a protein pocket, trained in four stages (large-scale ZINC20 chemical pretraining, ChEMBL bioactivity-oriented adaptation, pocket-sequence-conditioned fine-tuning, and pocket-specific docking-guided reinforcement learning with AutoDock Vina-based rewards), achieving competitive docking-oriented performance under a standardized evaluation protocol while maintaining chemical plausibility and Lipinski-based drug-likeness, with docking studies on Alzheimer's disease-associated targets and token-level analyses supporting its utility for de novo drug design.
This work introduces PockLigGPT, a GPT-based framework for ligand generation conditioned on the amino acid sequence of a protein pocket, trained in four stages (large-scale ZINC20 chemical pretraining, ChEMBL bioactivity-oriented adaptation, pocket-sequence-conditioned fine-tuning, and pocket-specific docking-guided reinforcement learning with AutoDock Vina-based rewards), achieving competitive docking-oriented performance under a standardized evaluation protocol while maintaining chemical plausibility and Lipinski-based drug-likeness, with docking studies on Alzheimer's disease-associated targets and token-level analyses supporting its utility for de novo drug design.