De novo design with deep generative models based on 3D similarity scoring. (15th August 2021)

• Record Type: Journal Article
• Authors: Papadopoulos, Kostas; Giblin, Kathryn A.; Janet, Jon Paul; Patronov, Atanas; Engkvist, Ola
• Is Part Of: Bioorganic & medicinal chemistry. Volume 44(2021)
• Publication Date: 2021-08-15
• Subjects: Deep learning -- Generative models -- Reinforcement learning -- DRD2 -- QSAR -- 3D similarity -- Shape similarity; Bioorganic chemistry -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; Chemistry, Clinical -- Periodicals; Chemistry, Organic -- Periodicals; Chimie bio-organique -- Périodiques; Chimie pharmaceutique -- Périodiques; 615.19
• DOI: 10.1016/j.bmc.2021.116308
• Languages: English
• ISSNs: 0968-0896

Abstract

Graphical abstract: Abstract: We have demonstrated the utility of a 3D shape and pharmacophore similarity scoring component in molecular design with a deep generative model trained with reinforcement learning. Using Dopamine receptor type 2 (DRD2) as an example and its antagonist haloperidol 1 as a starting point in a ligand based design context, we have shown in a retrospective study that a 3D similarity enabled generative model can discover new leads in the absence of any other information. It can be efficiently used for scaffold hopping and generation of novel series. 3D similarity based models were compared against 2D QSAR based, indicating a significant degree of orthogonality of the generated outputs and with the former having a more diverse output. In addition, when the two scoring components are combined together for training of the generative model, it results in more efficient exploration of desirable chemical space compared to the individual components.