Public articles linked to the same research event.
bioRxiv This work presents LLMsFold, a computational framework that identifies binding pockets geometrically, has Llama-3.3-70B generate candidate small molecules as SMILES strings, evaluates each with the Boltz-2 co-folding model for bound pose and binding affinity, and iteratively refines candidates through a feedback loop, yielding molecules for ACVR1 and CD19 that pass drug-likeness, synthetic accessibility, and novelty filters, with the ACVR1 candidate reaching a predicted affinity probability of 0.953 and predicted pIC50 of about 10.72 and the CD19 Pocket 1 candidate reaching a predicted pIC50 of about 7.73.
This work presents LLMsFold, a computational framework that identifies binding pockets geometrically, has Llama-3.3-70B generate candidate small molecules as SMILES strings, evaluates each with the Boltz-2 co-folding model for bound pose and binding affinity, and iteratively refines candidates through a feedback loop, yielding molecules for ACVR1 and CD19 that pass drug-likeness, synthetic accessibility, and novelty filters, with the ACVR1 candidate reaching a predicted affinity probability of 0.953 and predicted pIC50 of about 10.72 and the CD19 Pocket 1 candidate reaching a predicted pIC50 of about 7.73.
This work presents LLMsFold, a computational framework that identifies binding pockets geometrically, has Llama-3.3-70B generate candidate small molecules as SMILES strings, evaluates each with the Boltz-2 co-folding model for bound pose and binding affinity, and iteratively refines candidates through a feedback loop, yielding molecules for ACVR1 and CD19 that pass drug-likeness, synthetic accessibility, and novelty filters, with the ACVR1 candidate reaching a predicted affinity probability of 0.953 and predicted pIC50 of about 10.72 and the CD19 Pocket 1 candidate reaching a predicted pIC50 of about 7.73.
This work presents LLMsFold, a computational framework that identifies binding pockets geometrically, has Llama-3.3-70B generate candidate small molecules as SMILES strings, evaluates each with the Boltz-2 co-folding model for bound pose and binding affinity, and iteratively refines candidates through a feedback loop, yielding molecules for ACVR1 and CD19 that pass drug-likeness, synthetic accessibility, and novelty filters, with the ACVR1 candidate reaching a predicted affinity probability of 0.953 and predicted pIC50 of about 10.72 and the CD19 Pocket 1 candidate reaching a predicted pIC50 of about 7.73.