Machine Learning-Based Enhanced Drug Delivery System and Its Applications – A Systematic Review

Abhijeet  Madhukar Haval ^1*, Kumar Shwetabh ¹, Sushree Sasmita Dash ¹

Machine Learning (ML) transforms drug development, predicting drug properties, targets, interactions, and facilitating efficient exploration for researchers and pharmaceutical companies.

Abstract

Machine Learning (ML) methods offer advanced algorithms and tools to enhance drug development. Quantitative Structure-Activity Relationship (QSAR) approaches have successfully predicted several physicochemical aspects of pharmaceuticals, including toxicity, intake, drug-drug interactions, carcinogenesis, and dispersion.  ML, a branch of artificial intelligence, has demonstrated significant promise in drug development. The methods presented in this study can model non-linear databases and handle big data that is becoming more extensive and intricate. Diverse ML methodologies are currently employed for making forecasts of drug targets, modeling the framework of drug targets, forecasting binding sites, conducting ligand-based similarity searches, designing novel ligands with specific properties, creating scoring algorithms for molecular docking, constructing QSAR models for predicting biological reactions, and forecasting the pharmacokinetic and pharmacodynamic characteristics of ligands. The findings of this study illustrate the widespread utilization of ML techniques in drug discovery, suggesting a favorable outlook for these advances. These findings have the potential to facilitate further exploration of ML in connection with drug discovery and growth by researchers, learners, and pharmaceutical companies.

Keywords: Drug Delivery, Pharmaceutical Industry, Machine Learning, Applications

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