Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

Abstract

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

“…To further confirm the value of our approach, we also used the mean percentile ranking (MPR), an evaluation index based on recall, to evaluate the performance of the algorithm. This evaluation index has been applied in recommendation algorithm and analyses of the performance for predicting drug-targets (Hu et al, 2008;Johnson, 2014;Li et al, 2015;Ding et al, 2017;Hao et al, 2019;Liu et al, 2019b;Liu et al, 2019c; and disease biomarkers (Chen et al, 2016;Zeng et al, 2016;Hong et al, 2019;Xu et al, 2019). For each disease, the genes were ranked in descending order according to the calculated gene-disease predictive value.…”

Detecting drug communities and predicting comprehensive drug–drug interactions via balance regularized semi-nonnegative matrix factorization

“…The smaller the value, the better the prediction. More technical details about MPR can be found in [37, 38].…”

CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical Databases

“…Drug-target relationships have far-reaching implications and are of central importance in drug repurposing [32] and drug design research in general. [33] It was found that both databases have a comparable number of drug-target relationships: 27,900 in ChEMBL and 27,613 in DrugBank.…”