Beam search for automated design and scoring of novel ROR ligands with machine intelligence

  • Chemical language models enable de novo drug design without the requirement for explicit molecular construction rules. While such models have been applied to generate novel compounds with desired bioactivity, the actual prioritization and selection of the most promising computational designs remains challenging. Herein, we leveraged the probabilities learnt by chemical language models with the beam search algorithm as a model-intrinsic technique for automated molecule design and scoring. Prospective application of this method yielded novel inverse agonists of retinoic acid receptor-related orphan receptors (RORs). Each design was synthesizable in three reaction steps and presented low-micromolar to nanomolar potency towards RORγ. This model-intrinsic sampling technique eliminates the strict need for external compound scoring functions, thereby further extending the applicability of generative artificial intelligence to data-driven drug discovery.
Metadaten
Author:Michael Moret, Moritz Helmstädter, Francesca Grisoni, Gisbert Schneider, Daniel MerkORCiDGND
URN:urn:nbn:de:hebis:30:3-639259
DOI:https://doi.org/10.1002/anie.202104405
ISSN:1521-3773
Parent Title (English):Angewandte Chemie
Publisher:Wiley-VCH
Place of publication:Weinheim
Document Type:Article
Language:English
Date of Publication (online):2021/06/24
Date of first Publication:2021/06/24
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2022/04/05
Tag:deep learning; de novo design; drug discovery; neural network; nuclear receptor
Volume:60
Issue:35
Page Number:6
First Page:19477
Last Page:19482
Note:
A previous version of this manuscript has been deposited on a preprint server (https://doi.org/10.26434/chemrxiv.14153408.v1).
Note:
This research was supported by the Swiss National Science Foundation (grant no. 205321_182176 to G.S.), the RETHINK initiative at ETH Zurich, and the Novartis Forschungsstiftung (FreeNovation grant “AI in Drug Discovery” to G.S.). Open access funding enabled and organized by Projekt DEAL.
HeBIS-PPN:493370021
Institutes:Biochemie, Chemie und Pharmazie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0