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Related Experiment Video

Updated: Jan 7, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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AgentMol: Multi-Model AI System for Automatic Drug-Target Identification and Molecule Development.

Piotr Karabowicz1, Radosław Charkiewicz1,2, Alicja Charkiewicz3

  • 1Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269 Bialystok, Poland.

Methods and Protocols
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

AgentMol, an AI system, accelerates drug discovery by identifying protein targets and generating novel drug candidates. It uses large language models and deep learning for efficient molecular design and affinity prediction.

Keywords:
GPT-2LangGraph agentchemical language modelconvolutional neural networksdrug discovery

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Molecular modeling

Background:

  • Drug discovery is a lengthy and expensive undertaking.
  • Innovative computational methods are crucial for accelerating early-stage research.
  • AI offers potential solutions for optimizing target identification and compound development.

Purpose of the Study:

  • To introduce AgentMol, a novel AI system designed to automate and enhance the drug discovery pipeline.
  • To integrate large language models, chemical language modeling, and deep learning for efficient drug candidate generation and evaluation.
  • To provide a scalable and interpretable platform for AI-driven molecular discovery.

Main Methods:

  • AgentMol utilizes a Retrieval-Augmented Generation system with a Large Language Model for disease-related target identification.
  • A GPT-2-based chemical language model generates small-molecule candidates (SMILES format) conditioned on protein sequences.
  • A regression convolutional neural network (RCNN) predicts drug-target binding affinities (pKi).
  • LangGraph is employed for system orchestration, ensuring scalability and interpretability.

Main Results:

  • The chemical language model demonstrated high performance metrics: validity (1.00), uniqueness (0.96), and diversity (0.89).
  • The RCNN model achieved robust predictive accuracy for binding affinities, with R² > 0.6 and Pearson's R > 0.8.
  • AgentMol successfully enabled end-to-end generation and evaluation of drug candidates.
  • The system operates with accessible computational demands.

Conclusions:

  • AgentMol represents a significant advancement in AI-driven drug discovery.
  • The integrated AI system streamlines target identification, molecule generation, and affinity prediction.
  • This approach offers a practical and efficient pathway for developing novel drug candidates.