C-2-07

Predicting bioactivity of compound-drug target protein
pairs using support vector regression models reflecting
ligand efficiency
Nobuyoshi Sugaya 1
sugaya@pharmadesign.co.jp

1

Drug Discovery Department, Research & Development Division, PharmaDesign, In c.,
2-19-8, Hatchobori, Chuo-ku, Tokyo 104-0032, Japan

Keywords: Support vector regression, G-protein coupled receptor, Protein kinase, Ion channel,
ligand efficiency
Predicting bioactivity of compounds to drug target proteins using machine learning met hods is
one of the most intensively studied area in drug discovery and development. Although many
previous machine learning studies have succeeded in predicting novel ligand -protein interactions
with high performance, all of the previous studies to date ha ve been heavily dependent on thesimple use of raw bioactivity data of ligand potencies measured by IC50, EC50, Ki, and Kd
deposited in databases. In our previous study [1], we have showed that, using support vector
machines, binary classification models based on training data reflecting one of the representativesof ligand efficiency, Binding Efficiency Index (BEI) [2] can offer better performance in classifying
active and inactive compound-protein pairs than models based on training data reflecting IC50 orKi. In this study, we report that this result holds also when support vector regression (SVR) models
are applied to bioactivity data.
Utilizing bioactivity data measured by IC50 in GPCRSARfari ver. 2, KinaseSARfari ver. 4, andChEMBL 14 databases [3], we retrieved bioactivity data associated with G protein -coupled
receptors, protein kinases, and ion channels and created four types of training data; IC50 -based,pIC50-based, BEI-based, and Surface Efficiency Index (SEI) -based. Values of pIC50, BEI, and SEI
were transferred from observed values of IC50 in the databases. The number of instances in thetraining data is shown in Table 1. To represent compound -protein pairs in the training data, threekinds of compound descriptors (MACCS, 2D descriptors in MOE, a nd OpenBabel FP2) and single
protein descriptor (frequencies of dimmers of amino acid in protein sequence) were used. FromGPCRSARfari ver. 3, KinaseSARfari ver. 5.01, and ChEMBL 15 databases, we collected newly
added bioactivity data and used the data as validation data for evaluating the performance of theconstructed SVR models. Objective comparisons of the performance of the SVR models showedthat their prediction capabilities follow an order of SEI > BEI > pIC50 > IC50 as a whole. This
result is independent of compound descriptors used and drug target protein families. Thesuperiority of ligand efficiency-based SVR models may be partially attributed to distinct
distribution patterns of pIC50s, BEIs, and SEIs, showing narrower range of BEIs than pIC50 s andSEIs than BEIs.
[1] Sugaya, N., Training based on ligand efficiency improves prediction of bioactivities of ligands
and drug target proteins in a machine learning approach , Journal of Chemical Information andModeling, in press.
[2] Abad-Zapatero, C. and Metz, J. T., Ligand efficiency indices as guideposts for drug discovery,
Drug Discovery Today, 10:464-469, 2005.
[3] Gaulton, A., et al., ChEMBL: a large-scale bioactivity database for drug discovery, Nucleic
Acids Research, 40:D1100-D1107, 2012.