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DNMG: Deep molecular generative model by fusion of 3D information for de novo drug design by Tao Song; Yongqi Ren; Shuang Wang; Peifu Han; Lulu Wang; Xue Li; Alfonso Rodriguez-Patón is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is DNMG: Deep molecular generative model by fusion of 3D information for de novo drug design about?

Deep learning is improving and changing the process of de novo molecular design at a rapid pace. In recent years, great progress has been made in drug discovery and development by using deep generative models for de novo molecular design. However, most of the existing methods are string-based or graph-based and are limited by the lack of some very important properties, such as the three-dimensional information of molecules. We propose DNMG, a deep generative adversarial network (GAN) combined with transfer learning. Specifically, we use a Wasserstein-variant GAN based network architecture that considers the 3D grid spatial information of the ligand with atomic physicochemical properties to generate a representation of the molecule, which is then parsed into SMILES strings using an improved captioning network. Comprehensive in experiments demonstrate the ability of DNMG to generate valid and novel drug-like ligands. The DNMG model is used to design inhibitors for three targets, MK14, FNTA, and CDK2. The computational results show that the molecules generated by DNMG have better binding ability to the target proteins and better physicochemical properties. Overall, our deep generative

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Author

Tao Song; Yongqi Ren; Shuang Wang; Peifu Han; Lulu Wang; Xue Li; Alfonso Rodriguez-Patón

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)