AgentMol: Multi-Model AI System for Automatic Drug-Target Identification and Molecule Development.

ABSTRACT: Drug discovery remains a time-consuming and costly process, necessitating innovative computational approaches to accelerate early stage target identification and compound development. We introduce AgentMol, a modular multimodel AI system that integrates large language models, chemical language modeling, and deep learning-based affinity prediction to automate the discovery pipeline. AgentMol begins with disease-related queries processed through a Retrieval-Augmented Generation system using the Large Language Model to identify protein targets. Protein sequences are then used to condition a GPT-2-based chemical language model, which generates corresponding small-molecule candidates in SMILES format. Finally, a regression convolutional neural network (RCNN) predicts the drug-target interaction

SUBMITTER: Karabowicz P 

PROVIDER: S-EPMC12736193 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature