A Methodology for the Prediction of Drug Target Interaction using CDK Descriptors

TL;DR

This paper introduces a new computational model for predicting drug-target interactions using CDK descriptors, demonstrating superior performance over existing methods through evaluation on benchmark datasets.

Contribution

The study presents a novel DTI prediction approach utilizing CDK descriptors, outperforming traditional fingerprints and previous techniques in accuracy.

Findings

CDK descriptors outperform other fingerprints in DTI prediction

The proposed model significantly outperforms existing methods

Evaluation on benchmark data confirms the effectiveness of the approach

Abstract

Detecting probable Drug Target Interaction (DTI) is a critical task in drug discovery. Conventional DTI studies are expensive, labor-intensive, and take a lot of time, hence there are significant reasons to construct useful computational techniques that may successfully anticipate possible DTIs. Although certain methods have been developed for this cause, numerous interactions are yet to be discovered, and prediction accuracy is still low. To meet these challenges, we propose a DTI prediction model built on molecular structure of drugs and sequence of target proteins. In the proposed model, we use Simplified Molecular Input Line Entry System (SMILES) to create CDK descriptors, Molecular ACCess System (MACCS) fingerprints, Electrotopological state (Estate) fingerprints and amino acid sequences of targets to get Pseudo Amino Acid Composition (PseAAC). We target to evaluate performance of DTI prediction models using CDK descriptors. For comparison, we use benchmark data and evaluate models performance on two widely used fingerprints, MACCS fingerprints and Estate fingerprints. The evaluation of performances shows that CDK descriptors are superior at predicting DTIs. The proposed method also outperforms other previously published techniques significantly.