Literature DB >> 35544331

Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models.

Weixin Xie1, Fanhao Wang1, Yibo Li2, Luhua Lai1,3, Jianfeng Pei1.   

Abstract

A persistent goal for de novo drug design is to generate novel chemical compounds with desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models provide alternative routes to this goal. Numerous model architectures and optimization strategies have been explored in recent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aiming at three-dimensional (3D) molecule generation have also been proposed, gaining attention for their unique advantages and potential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developments in 3D molecular generative models combined with deep learning and discusses future directions for de novo drug design.

Entities:  

Keywords:  de novo drug design; deep learning; generative model; structure-based drug design; structure-based generation; three-dimentional generation

Mesh:

Year:  2022        PMID: 35544331     DOI: 10.1021/acs.jcim.2c00042

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   6.162


  2 in total

1.  A pocket-based 3D molecule generative model fueled by experimental electron density.

Authors:  Lvwei Wang; Rong Bai; Xiaoxuan Shi; Wei Zhang; Yinuo Cui; Xiaoman Wang; Cheng Wang; Haoyu Chang; Yingsheng Zhang; Jielong Zhou; Wei Peng; Wenbiao Zhou; Bo Huang
Journal:  Sci Rep       Date:  2022-09-06       Impact factor: 4.996

2.  Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration.

Authors:  Keerthi Krishnan; Ryan Kassab; Steve Agajanian; Gennady Verkhivker
Journal:  Int J Mol Sci       Date:  2022-09-24       Impact factor: 6.208

  2 in total

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