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Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models
被引:28
|作者:
Xie, Weixin
[1
]
Wang, Fanhao
[1
]
Li, Yibo
[2
]
Lai, Luhua
[1
,3
]
Pei, Jianfeng
[1
]
机构:
[1] Peking Univ, Acad Adv Interdisciplinary Studies, Ctr Quantitat Biol, Beijing 100871, Peoples R China
[2] Peking Univ, Acad Adv Interdisciplinary Studies, Ctr Life Sci, Beijing 100871, Peoples R China
[3] Peking Univ, Coll Chem & Mol Engn, Peking Tsinghua Ctr Life Sci BNLMS, Beijing 100871, Peoples R China
基金:
中国国家自然科学基金;
关键词:
de novo drug design;
deep learning;
generative model;
three-dimentional generation;
structure-based generation;
structure-based drug design;
INFORMATION;
D O I:
10.1021/acs.jcim.2c00042
中图分类号:
R914 [药物化学];
学科分类号:
100701 ;
摘要:
A persistent goal forde novodrug design is togenerate novel chemical compounds with desirable properties in alabor-, time-, and cost-efficient manner. Deep generative modelsprovide alternative routes to this goal. Numerous modelarchitectures and optimization strategies have been explored inrecent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aimingat three-dimensional (3D) molecule generation have also beenproposed, gaining attention for their unique advantages andpotential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developmentsin 3D molecular generative models combined with deep learningand discusses future directions forde novodrug design.
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页码:2269 / 2279
页数:11
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