Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

TLDR

Drug‑target interaction networks are crucial for drug development, yet experimental determination is time‑consuming and costly. The study aims to develop an in silico prediction method that encodes drug compounds by functional groups and proteins by biological features to rapidly identify drug‑target interactions. The authors use mRMR for optimal feature selection, partition proteins into enzymes, ion channels, GPCRs, and nuclear receptors, and build four nearest‑neighbor predictors, each targeting one protein group. Cross‑validation shows the four predictors achieve success rates of 85.48%, 80.78%, 78.49%, and 85.66%, indicating a promising network prediction system.

Abstract

Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.Our results indicate that the network prediction system thus established is quite promising and encouraging.