.png){kind=icon}
返回
Multi-Assay-Based Compound Prioritization via Assistance Utilization: A Machine Learning Framework
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jcim.6b00737.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Effective prioritization of chemical compounds that show promising bioactivities from compound screenings represents a first critical step toward identifying successful drug candidates. Current development on computational approaches for compound prioritization is largely focused on devising advanced ranking algorithms that better learn the ordering among compounds. However, such methodologies are fundamentally limited by the scarcity of available data, particularly when the screenings are conducted at a relatively small scale over known promising compounds. Instead, in this work, we explore the structures of bioassay space and leverage such structures to improve ranking performance of an existing strong ranking algorithm. This is done by identifying assistance bioassays and assistance compounds intelligently and leveraging such assistance within the existing ranking algorithm. By leveraging the assistance bioassays and assistance compounds, the data scarcity can be properly compromised. Along this line, we develop a suite of assistance bioassay selection methods and assistance compound selection methods. Our experiments demonstrate an overall 8.34% improvement on the ranking performance over the state of the art.
Keyword:
SUPPORT VECTOR MACHINES
DRUG DISCOVERY
BIOLOGICAL-ACTIVITY
LIGAND-BINDING
PREDICTION
AFFINITY
FINGERPRINT
SELECTIVITY
RECEPTORS
DIVERSITY
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:
5.3
论文数:
9.1K
被引数:
4.0W
机构
引用论文
.png){kind=icon}
P2‐233: The impact of familiarity on the usability of everyday products for older adults with Alzheimer's diseaseP2‐233：熟悉度对阿尔茨海默病老年人日常产品可用性的影响
OAAI
OAAI分享收藏Characterization of polymer matrix and low melting point solder for anisotropic conductive film各向异性导电膜用聚合物基体及低熔点焊料的表征
PREAI
分享收藏
查看更多内容