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Machine Learning in Chemoinformatics and Medicinal Chemistry.

Raquel Rodríguez-Pérez1,2, Filip Miljković1,3, Jürgen Bajorath1

  • 1Department of Life Science Informatics, B-IT (Bonn-Aachen International Center for Information Technology), Chemical Biology and Medicinal Chemistry Program Unit, LIMES (Life and Medical Sciences Institute), Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany;

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Machine learning and deep learning are advancing pharmaceutical research by improving in silico predictions and drug design. This review covers new applications, opportunities, and challenges in chemoinformatics and medicinal chemistry.

Keywords:
chemoinformaticsdata structuresdeep learninglearning strategiesmachine learningmedicinal chemistry

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Area of Science:

  • Chemoinformatics
  • Medicinal Chemistry
  • Computational Drug Discovery

Background:

  • Machine learning (ML) is a key approach in chemoinformatics and medicinal chemistry.
  • Advancements in computational power and deep learning (DL) algorithms have significantly enhanced ML capabilities.
  • Big data emergence fuels progress in pharmaceutical research applications.

Purpose of the Study:

  • To review novel applications of ML and DL in chemoinformatics and medicinal chemistry.
  • To discuss opportunities and challenges associated with new ML/DL methods.
  • To emphasize the importance of baseline comparisons, validation, and applicability domains.

Main Methods:

  • Review of recent literature on machine learning and deep learning applications.
  • Analysis of algorithmic developments and big data impact.
  • Discussion of validation methodologies and applicability domains.

Main Results:

  • ML and DL are driving significant advancements in compound activity prediction, de novo drug design, and reaction modeling.
  • Novel applications are emerging due to new algorithms and increased computational resources.
  • The field benefits from the integration of big data.

Conclusions:

  • ML and DL offer powerful tools for addressing complex challenges in pharmaceutical research.
  • Further development requires rigorous validation and careful consideration of applicability.
  • Opportunities exist for innovative methods and applications in drug discovery.