DRUG–TARGET INTERACTION PREDICTION USING BIOINFORMATICS AND COMPUTATIONAL BIOLOGY APPROACHES

Abstract

Drug–target interaction prediction has become an important area of research in computational biology and bioinformatics because of its significant contribution to modern drug discovery and pharmacological development. Conventional experimental approaches used for identifying interactions between pharmaceutical compounds and biological targets are often time-consuming, expensive, and labor-intensive, thereby increasing the importance of computational prediction systems capable of accelerating therapeutic research. The present study examined the role of bioinformatics and computational biology approaches in predicting drug–target interactions using large-scale biological
datasets and computational analytical frameworks. The research adopted a qualitative and computational analytical design based on secondary scientific literature and the BindingDB for Drug–Target Interaction dataset. The dataset consisted of approximately 2.08 million interaction records containing compound structures represented through SMILES notation, protein target sequences, and experimentally validated affinity values expressed as pKd measurements. The findings demonstrated that computational methodologies, including machine learning, deep learning, and graph-based
analytical systems, significantly improved predictive interaction analysis by identifying molecular relationships between drugs and biological targets. Deep learning frameworks enhanced scalability and automated feature extraction, while machine learning systems improved interaction classification and predictive efficiency across multidimensional biological datasets. The study further revealed that binding affinity analysis provided important quantitative indicators for evaluating molecular interaction strength and therapeutic relevance. Despite these advantages, several limitations, including biological complexity, heterogeneous molecular structures, data imbalance, and dependence on high-quality
datasets, continued to affect predictive reliability and computational interpretability. The BindingDB dataset demonstrated strong applicability for computational pharmacology and bioinformatics research due to its large-scale molecular interaction records and structured biological information. Overall, the study concluded that computational biology and bioinformatics approaches have become indispensable tools in modern drug discovery and predictive pharmacology. The integration of computational prediction systems, biological databases, and advanced analytical methodologies may significantly improve therapeutic discovery, pharmacological screening, and precision medicine research in future biomedical sciences.