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Ranking chemical structures for drug discovery: a new machine learning approach.

Abstract：

:With chemical libraries increasingly containing millions of compounds or more, there is a fast-growing need for computational methods that can rank or prioritize compounds for screening. Machine learning methods have shown considerable promise for this task; indeed, classification methods such as support vector machines (SVMs), together with their variants, have been used in virtual screening to distinguish active compounds from inactive ones, while regression methods such as partial least-squares (PLS) and support vector regression (SVR) have been used in quantitative structure-activity relationship (QSAR) analysis for predicting biological activities of compounds. Recently, a new class of machine learning methods - namely, ranking methods, which are designed to directly optimize ranking performance - have been developed for ranking tasks such as web search that arise in information retrieval (IR) and other applications. Here we report the application of these new ranking methods in machine learning to the task of ranking chemical structures. Our experiments show that the new ranking methods give better ranking performance than both classification based methods in virtual screening and regression methods in QSAR analysis. We also make some interesting connections between ranking performance measures used in cheminformatics and those used in IR studies.

journal_name

J Chem Inf Model

journal_title

Journal of chemical information and modeling

authors

Agarwal S,Dugar D,Sengupta S

doi

10.1021/ci9003865

subject

Has Abstract

pub_date

2010-05-24 00:00:00

pages

716-31

issue

5

eissn

1549-9596

issn

1549-960X

journal_volume

50

pub_type

杂志文章

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