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Oestreich, M.A.* ; Merdivan, E. ; Lee, M.* ; Schultze, J.L.* ; Piraud, M. ; Becker, M.*

DrugDiff: Small molecule diffusion model with flexible guidance towards molecular properties.

J. Cheminformatics 17:23 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
With the cost/yield-ratio of drug development becoming increasingly unfavourable, recent work has explored machine learning to accelerate early stages of the development process. Given the current success of deep generative models across domains, we here investigated their application to the property-based proposal of new small molecules for drug development. Specifically, we trained a latent diffusion model-DrugDiff-paired with predictor guidance to generate novel compounds with a variety of desired molecular properties. The architecture was designed to be highly flexible and easily adaptable to future scenarios. Our experiments showed successful generation of unique, diverse and novel small molecules with targeted properties. The code is available at https://github.com/MarieOestreich/DrugDiff . SCIENTIFIC CONTRIBUTION: This work expands the use of generative modelling in the field of drug development from previously introduced models for proteins and RNA to the here presented application to small molecules. With small molecules making up the majority of drugs, but simultaneously being difficult to model due to their elaborate chemical rules, this work tackles a new level of difficulty in comparison to sequence-based molecule generation as is the case for proteins and RNA. Additionally, the demonstrated framework is highly flexible, allowing easy addition or removal of considered molecular properties without the need to retrain the model, making it highly adaptable to diverse research settings and it shows compelling performance for a wide variety of targeted molecular properties.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Drug Development ; Generative Modelling ; Latent Diffusion ; Targeted Generation; Prediction; Discovery; Zinc
e-ISSN 1758-2946
Zeitschrift Journal of Cheminformatics
Quellenangaben Band: 17, Heft: 1, Seiten: , Artikelnummer: 23 Supplement: ,
Verlag BioMed Central
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Förderungen BMBF
EU
Helmholtz Association's Initiative and Networking Fund on the HAICORE@FZJ partition
HGF Helmholtz AI
Helmholtz Artificial Intelligence Cooperation Unit