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Protocol to generate dual-target compounds using a transformer chemical language model.

Sanjana Srinivasan1, Jürgen Bajorath1

  • 1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany; Lamarr Institute for Machine Learning and Artificial Intelligence, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany.

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Summary

This study introduces a novel protocol for creating dual-target compounds (DT-CPDs) that bind to two proteins simultaneously. The method utilizes a transformer-based chemical language model for efficient drug discovery.

Keywords:
ChemistryComputer sciencesMolecular/Chemical ProbesSystems biology

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Developing compounds that target multiple proteins is crucial for complex diseases.
  • Existing methods for generating dual-target compounds (DT-CPDs) can be inefficient.
  • Transformer-based chemical language models offer a promising avenue for de novo drug design.

Purpose of the Study:

  • To present a detailed protocol for generating DT-CPDs using a transformer-based chemical language model.
  • To guide researchers in preparing data and pre-training models for DT-CPD generation.
  • To enable the creation of novel chemical entities with dual-target specificity.

Main Methods:

  • Utilized a transformer-based chemical language model for compound generation.
  • Pre-trained the model on datasets of single-target compounds (ST-CPDs) and DT-CPDs.
  • Developed procedures for data assembly and model evaluation on specific protein pairs.
  • Included software installation and data preparation steps.

Main Results:

  • Successfully outlined a protocol for generating DT-CPDs.
  • Demonstrated the feasibility of using transformer models for dual-target compound design.
  • Provided a framework for evaluating model performance on hold-out test sets.

Conclusions:

  • The presented protocol offers a systematic approach to designing dual-target compounds.
  • Transformer-based chemical language models are effective tools for advancing DT-CPD discovery.
  • This work facilitates the development of novel therapeutics with enhanced specificity and efficacy.