This article is part of the supplement: Ninth Annual MCBIOS Conference. Dealing with the Omics Data Deluge
Implementation of multiple-instance learning in drug activity prediction
1 Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University, 38677, USA
2 Department of Computer and Information Science, School of Engineering, University of Mississippi, University, 38677, USA
3 Research Institute of Pharmaceutical Sciences, School of Pharmacy, University of Mississippi, University, 38677, USA
4 Department of Genetics, Washington University School of Medicine, 4444 Forest Park Blvd., Campus Box 8510, MO 63108, USA
Citation and License
BMC Bioinformatics 2012, 13(Suppl 15):S3 doi:10.1186/1471-2105-13-S15-S3Published: 11 September 2012
In 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.
We 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.
The 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.
The 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.