Structure-Based Drug Discovery with Deep Learning**

R. Özçelik, D. van Tilborg, J. Jiménez-Luna, F. Grisoni (Corresponding author)

Onderzoeksoutput: Bijdrage aan tijdschriftArtikel recenserenpeer review

17 Citaten (Scopus)
45 Downloads (Pure)

Samenvatting

Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep-learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a renaissance in structure-based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.

Originele taal-2Engels
Artikelnummere202200776
Aantal pagina's13
TijdschriftChemBioChem
Volume24
Nummer van het tijdschrift13
DOI's
StatusGepubliceerd - 3 jul. 2023

Bibliografische nota

Publisher Copyright:
© 2023 The Authors. ChemBioChem published by Wiley-VCH GmbH.

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