Combining Network-based and Matrix Factorization to Predict Novel Drug-target Interactions: A Case Study Using the Brazilian Natural Chemical Database

Abstract

Background: Chemogenomic techniques use mathematical calculations to predict new drug-target interactions (DTIs) based on drugs' chemical and biological information and pharmacological targets. Compared to other structure-based computational methods, they are faster and less expensive. Network analysis and matrix factorization are two practical chemogenomic approaches for predicting DTIs from many drugs and targets. However, despite the extensive literature introducing various chemogenomic techniques and methodologies, there is no consensus for predicting interactions using a drug or a target, a set of drugs, and a dataset of known interactions. Methods: This study predicted novel DTIs from a limited collection of drugs using a heterogeneous ensemble based on network and matrix factorization techniques. We examined three network-based approaches and two matrix factorization-based methods on benchmark datasets. Then, we used one network approach and one matrix factorization technique on a small collection of Brazilian plant-derived pharmaceuticals. Results: We have discovered two novel DTIs and compared them to the Therapeutic Target Database to detect linked disorders, such as breast cancer, prostate cancer, and Cushing syndrome, with two drugs (Quercetin and Luteolin) originating from Brazilian plants. Conclusions: The suggested approach allows assessing the performance of approaches only based on their sensitivity, independent of their unfavorable interactions. Findings imply that integrating network and matrix factorization results might be a helpful technique in bioinformatics investigations involving the development of novel medicines from a limited range of drugs.

A Comparative Analysis of Computational Drug Repurposing Approaches – Proposing a Novel Tensor-Matrix-Tensor Factorization Method

“…Matrix and tensor decomposition methods have evolved due to either utilizing the available information or having complementary information. In other words, the matrix factorization methods have evolved to more complicated versions to extract the highly predictive features or act computationally efficient [22,23,24]. This is true for tensor factorization methods or their combination with matrix factorization methods as well.…”