Advances in de novo drug design: From conventional to machine learning methods

Varnavas D. Mouchlis, Antreas Afantitis, Angela Serra, Michele Fratello, Anastasios G. Papadiamantis, Vassilis Aidinis, Iseult Lynch, Dario Greco, Georgia Melagraki

Research output: Contribution to journalReview Articlepeer-review

Abstract

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure‐based and ligand‐based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement‐learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencod-ers. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine‐learning methodologies and highlights hot topics for further de-velopment.

Original languageEnglish
Article number1676
Number of pages22
JournalInternational Journal of Molecular Sciences
Volume22
Issue number4
DOIs
Publication statusPublished - Feb 2021
Publication typeA2 Review article in a scientific journal

Keywords

  • Artificial intelligence
  • Artificial neural networks
  • Autoencoders
  • Convolutional neural networks
  • De novo drug design
  • Deep reinforcement learning
  • Generative adversarial networks
  • Machine learning
  • Recurrent neural networks

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

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