PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs.

Journal: Journal of chemical information and modeling
PMID: 38504115

Abstract

Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for the de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.

Authors

  • Hazem Mslati
    Vancouver Prostate Centre, The University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada.
  • Francesco Gentile
    Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC V6H 3Z6, Canada.
  • Mohit Pandey
    Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, USA.
  • Fuqiang Ban
    Vancouver Prostate Centre, Department of Urologic Sciences , Faculty of Medicine, University of British Columbia , 2660 Oak Street , Vancouver , British Columbia V6H 3Z6 , Canada.
  • Artem Cherkasov
    Vancouver Prostate Centre, Department of Urologic Sciences , Faculty of Medicine, University of British Columbia , 2660 Oak Street , Vancouver , British Columbia V6H 3Z6 , Canada.

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