Deep learning methods for molecular representation and property prediction

被引：106

作者：

Li, Zhen ^{[1
]}

Jiang, Mingjian ^{[3
]}

Wang, Shuang ^{[4
]}

Zhang, Shugang ^{[2
]}

机构：

[1] Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Shandong, Peoples R China

[2] Ocean Univ China, Coll Comp Sci & Technol, Qingdao 266100, Shandong, Peoples R China

[3] Qingdao Univ Technol, Sch Informat & Control Engn, Qingdao 266033, Shandong, Peoples R China

[4] China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Shandong, Peoples R China

来源：

DRUG DISCOVERY TODAY | 2022年 / 27卷 / 12期

关键词：

Molecular representation; Deep learning; Self-supervised learning; Drug discovery; Property prediction; PERFORMANCE; NETWORK; SMILES; INFORMATION; TOPOLOGY;

D O I：

10.1016/j.drudis.2022.103373

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

With advances in artificial intelligence (AI) methods, computer-aided drug design (CADD) has developed rapidly in recent years. Effective molecular representation and accurate property prediction are crucial tasks in CADD workflows. In this review, we summarize contemporary applications of deep learning (DL) methods for molecular representation and property prediction. We categorize DL methods according to the format of molecular data (1D, 2D, and 3D). In addition, we discuss some common DL models, such as ensemble learning and transfer learning, and analyze the interpretability methods for these models. We also highlight the challenges and opportunities of DL methods for molecular representation and property prediction.

引用

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