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Protocol update to: Protocol to generate dual-target compounds using a transformer-based 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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This study introduces a protocol for generating multi-target compounds (dual- and triple-target compounds) using advanced chemical language models. The method enables precise drug design by targeting multiple proteins simultaneously.

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BioinformaticsChemistryComputer sciences

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Artificial intelligence in medicine

Background:

  • Developing novel compounds that interact with multiple protein targets is crucial for complex diseases.
  • Existing methods for designing multi-target compounds are often inefficient and time-consuming.
  • Transformer-based chemical language models offer a promising avenue for accelerating compound design.

Purpose of the Study:

  • To present a detailed protocol for generating dual-target compounds (DT-CPDs) and triple-target compounds (TT-CPDs).
  • To leverage transformer-based chemical language models for designing compounds that bind to two or three specific target proteins.
  • To provide a reproducible method for researchers in computational chemistry and drug discovery.

Main Methods:

  • Utilizing transformer-based chemical language models for compound generation.
  • Pre-training models on single-target compounds (ST-CPDs) and DT-CPDs.
  • Fine-tuning models on assembled ST-CPD and DT- or TT-CPD data for specific protein targets.
  • Evaluating model performance using hold-out test sets.

Main Results:

  • A comprehensive protocol for generating DT-CPDs and TT-CPDs has been established.
  • The protocol details software installation, data preparation, pre-training, fine-tuning, and evaluation.
  • The method enables the design of compounds targeting specific protein pairs or triplets.

Conclusions:

  • The presented protocol offers a systematic approach to designing multi-target compounds using AI.
  • This methodology can significantly advance drug discovery efforts by enabling precise targeting of multiple proteins.
  • The protocol is an updated and refined version of previous work, enhancing reproducibility and efficiency.