Transfer Learning for Drug Discovery
Transfer Learning for Drug Discovery
复制标题
药物发现的迁移学习
DOI：
10.1021/acs.jmedchem.9b02147
复制
发表时间：
2020-08-27
影响因子：
7.3
通讯作者：
Pei, Jianfeng
中科院分区：
文献类型：
--
作者：
Cai, Chenjing;Wang, Shiwei;Pei, Jianfeng
在电子药物发现工作中，可用于训练模型的数据集往往很小。事实上，标签数据的稀缺性是人工智能辅助药物发现的一大障碍。这个问题的一个解决方案是开发能够处理相对异质和稀缺数据的算法。迁移学习是一种机器学习，它可以利用来自其他相关任务的现有的、可概括的知识来实现对具有少量数据的单独任务的学习。深度迁移学习是药物发现领域中最常用的迁移学习类型。这一视角提供了迄今为止药物发现的迁移学习和相关应用的概述。此外，还对药物发现中迁移学习的未来发展进行了展望。
The data sets available to train models for in silico drug discovery efforts are often small. Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence-assisted drug discovery. One solution to this problem is to develop algorithms that can cope with relatively heterogeneous and scarce data. Transfer learning is a type of machine learning that can leverage existing, generalizable knowledge from other related tasks to enable learning of a separate task with a small set of data. Deep transfer learning is the most commonly used type of transfer learning in the field of drug discovery. This Perspective provides an overview of transfer learning and related applications to drug discovery to date. Furthermore, it provides outlooks on the future development of transfer learning for drug discovery.