计算机科学
代表(政治)
药物发现
人工智能
机器学习
领域(数学)
比例(比率)
生物
计算生物学
生物信息学
政治
量子力学
物理
法学
纯数学
数学
政治学
作者
Linhui Yu,Yansen Su,Yuansheng Liu,Xiangxiang Zeng
出处
期刊:Briefings in Functional Genomics
[Oxford University Press]
日期:2021-08-02
卷期号:20 (5): 323-332
被引量:20
摘要
In recent years, the computer-assisted techniques make a great progress in the field of drug discovery. And, yet, the problem of limited labeled data problem is still challenging and also restricts the performance of these techniques in specific tasks, such as molecular property prediction, compound-protein interaction and de novo molecular generation. One effective solution is to utilize the experience and knowledge gained from other tasks to cope with related pursuits. Unsupervised pretraining is promising, due to its capability of leveraging a vast number of unlabeled molecules and acquiring a more informative molecular representation for the downstream tasks. In particular, models trained on large-scale unlabeled molecules can capture generalizable features, and this ability can be employed to improve the performance of specific downstream tasks. Many relevant pretraining works have been recently proposed. Here, we provide an overview of molecular unsupervised pretraining and related applications in drug discovery. Challenges and possible solutions are also summarized.
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