.png){kind=icon}
返回
Inferring multi-target QSAR models with taxonomy-based multi-task learning
.png){kind=icon}
OA
AI
DOI：10.1186/1758-2946-5-33.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background: A plethora of studies indicate that the development of multi-target drugs is beneficial for complex diseases like cancer. Accurate QSAR models for each of the desired targets assist the optimization of a lead candidate by the prediction of affinity profiles. Often, the targets of a multi-target drug are sufficiently similar such that, in principle, knowledge can be transferred between the QSAR models to improve the model accuracy. In this study, we present two different multi-task algorithms from the field of transfer learning that can exploit the similarity between several targets to transfer knowledge between the target specific QSAR models. Results: We evaluated the two methods on simulated data and a data set of 112 human kinases assembled from the public database ChEMBL. The relatedness between the kinase targets was derived from the taxonomy of the humane kinome. The experiments show that multi-task learning increases the performance compared to training separate models on both types of data given a sufficient similarity between the tasks. On the kinase data, the best multi-task approach improved the mean squared error of the QSAR models of 58 kinase targets. Conclusions: Multi-task learning is a valuable approach for inferring multi-target QSAR models for lead optimization. The application of multi-task learning is most beneficial if knowledge can be transferred from a similar task with a lot of in-domain knowledge to a task with little in-domain knowledge. Furthermore, the benefit increases with a decreasing overlap between the chemical space spanned by the tasks.
Keyword:
Proteochemometrics
QSAR
Multi-target
Support vector machine
Kinome
Machine learning
Multi-task
Domain adaption
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.7
论文数:
1.5K
被引数:
1.1W
机构
引用论文
Low affinity channel blocking (uncompetitive) NMDA receptor antagonists as therapeutic agents - toward an understanding of their favorable tolerability.png)
AMINO ACIDS
AMINO ACIDSIF2.4
PREAI
分享收藏
查看更多内容