Implementation of multiple-instance learning in drug activity prediction

BMC Bioinformatics

Proceedings

Xiaofei Nan¹  Yixin Chen¹  Dawn E Wilkins¹  Gang Fu²  Pankaj R Daga²  Haining Liu²  Ronak Y Patel³  Robert J Doerksen⁴ 

[1] Department of Computer and Information Science, School of Engineering, University of Mississippi, 38677, University, USA;Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, 38677, University, USA;Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, 38677, University, USA;Department of Genetics, Washington University School of Medicine, Campus Box 8510, 4444 Forest Park Blvd., 63108, MO, USA;Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, 38677, University, USA;Research Institute of Pharmaceutical Sciences, School of Pharmacy, University of Mississippi, University, 38677, USA;

关键词: Pharmacophore Model;    Dissimilarity Measure;    Matthews Correlation Coefficient;    Pharmacophore Feature;    Bioactive Conformer;   

DOI  :  10.1186/1471-2105-13-S15-S3

来源: Springer

【 摘 要 】

BackgroundIn the context of drug discovery and development, much effort has been exerted to determine which conformers of a given molecule are responsible for the observed biological activity. In this work we aimed to predict bioactive conformers using a variant of supervised learning, named multiple-instance learning. A single molecule, treated as a bag of conformers, is biologically active if and only if at least one of its conformers, treated as an instance, is responsible for the observed bioactivity; and a molecule is inactive if none of its conformers is responsible for the observed bioactivity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule.MethodsWe encoded the 3-dimensional structures using pharmacophore fingerprints which are binary strings, and accomplished instance-based embedding using calculated dissimilarity distances. Four dissimilarity measures were employed and their performances were compared. 1-norm SVM was used for joint feature selection and classification. The approach was applied to four data sets, and the best proposed model for each data set was determined by using the dissimilarity measure yielding the smallest number of selected features.ResultsThe predictive abilities of the proposed approach were compared with three classical predictive models without instance-based embedding. The proposed approach produced the best predictive models for one data set and second best predictive models for the rest of the data sets, based on the external validations. To validate the ability of the proposed approach to find bioactive conformers, 12 small molecules with co-crystallized structures were seeded in one data set. 10 out of 12 co-crystallized structures were indeed identified as significant conformers using the proposed approach.ConclusionsThe proposed approach was proven not to suffer from overfitting and to be highly competitive with classical predictive models, so it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers.

【 授权许可】

Unknown   
© Fu et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.