A support vector machine approach to classify human cytochrome P450 3A4 inhibitors

Abstract

The cytochrome P450 (CYP) enzyme superfamily plays a major role in the metabolism of commercially available drugs. Inhibition of these enzymes by a drug may result in a plasma level increase of another drug, thus leading to unwanted drug-drug interactions when two or more drugs are coadministered. Therefore, fast and reliable in silico methods predicting CYP inhibition from calculated molecular properties are an important tool which can be applied to assess both already synthesized as well as virtual compounds. We have studied the performance of support vector machines (SVMs) to classify compounds according to their potency to inhibit CYP3A4. The data set for model generation consists of more than 1300 structural diverse drug-like research molecules which were divided into training and test sets. The predictive power of SVMs crucially depends on a careful selection of parameters specifying the kernel function and the penalty for misclassifications. In this study we have investigated a procedure to identify a valid set of SVM parameters which is based on a sampling of the parameter space on a regular grid. From this set of parameters, either single SVMs or SVM committees were trained to distinguish between strong and weak inhibitors or to achieve a more realistic three-class assignment, with one class representing medium inhibitors. This workflow was studied for several kernel functions and descriptor sets. All SVM models performed significantly better than PLS-DA models which were generated from the corresponding descriptor sets. As a very promising result, simple two-dimensional (2D) descriptors yield a three-class model which correctly classifies more than 70% of the test set. Our work illustrates that SVMs used in combination with simple 2D descriptors provide a very effective and reliable tool which allows a fast assessment of CYP3A4 inhibition potency in an early in silico filtering process.

OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification

“…Currently, a multitude of linear and non-linear multivariate classification approaches exist, such as support vector machines (SVMs) [1], Quadratic Discriminant Analysis (QDA) (see, for instance Reference [2]) and Partial Least Squares (PLS) [3] regression. Non-linear methods may occasionally outperform linear methods in terms of classification rates [4,5] but typically lack the powerful interpretational capabilities that have become the distinctive characteristics of linear methods such as PLS. The choice of method adopted might thus be related to whether discriminatory power is of greater importance than the ability to interpret the underlying chemical or biological changes related to class differences.…”

Classification of 5-HT1A receptor agonists and antagonists using GA-SVM method

“…However, large C values may lead to overfitting of the training set. The parameter r indicates the radial basis function (RBF), which is the kernel function used in this study [51] . Here, we optimized the value of C and r using our in-house method (detailed below) to build the best classification model.…”

Classification of Highly Unbalanced CYP450 Data of Drugs Using Cost Sensitive Machine Learning Techniques.

“…Tuning the parameters is very important and can be implemented by optimizing a certain quality measure, which is obtained in cross-validation steps. A robust quality measure for classifiers is the Matthews correlation coefficient mcc 16 …”

“…Therefore the in silico prediction of drug metabolism profiles of CYP450s has become one of the key technologies in early drug discovery support. [15][16][17][18][19][20][21][22][23][24] The primary aim of this paper is to demonstrate how to build useful classification models out of unbalanced [25][26][27][28] data sets. We consider a data set to be unbalanced if either the sizes of the two classes differ significantly, or the costs for a false negative classification are very high whereas a false positive is acceptable, or if both conditions hold.…”