Machine Learning in Drug Discovery: A Comprehensive Analysis of Applications, Challenges, and Future Directions

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dc.creatorKunduru, Arjun Reddy
dc.date2023-08-19
dc.date.accessioned2023-08-21T08:03:09Z
dc.date.available2023-08-21T08:03:09Z
dc.descriptionMachine learning has revolutionized drug discovery by speeding up the process and improving therapeutic interventions, transforming the pharmaceutical research and development landscape. The paper embarks on a meticulous journey, delving into the intricate fabric of machine learning's integration into drug discovery. It deftly navigates through the virtual corridors of compound screening and virtual screening, where machine learning algorithms intricately assess massive chemical libraries, substantially hastening the identification of potential drug candidates. The analysis extends to encompass quantitative structure-activity relationship (QSAR) modeling, predictive ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) modeling, de novo drug design, and target identification and validation, meticulously unraveling the pivotal role machine learning plays in each facet. Yet this transformative union does not come without its share of challenges. The paper uncovers the nuances of data quality and quantity, grapples with the intricacies of interpretability, and addresses the critical need to harmonize domain knowledge with data-driven methodologies. It illuminates the hurdles of transferability and generalization, coupled with the ethical and regulatory considerations that loom large over this cutting-edge convergence. Furthermore, this paper casts an anticipatory glance toward the future horizons of this symbiotic relationship between machine learning and drug discovery. It envisions a time when there will be explainable AI, multi-modal data integration, reinforcement learning for compound optimization, collaborative AI platforms, and strong ethical and regulatory frameworks. By synthesizing insights gleaned from a systematic review of existing literature, this paper aims to spotlight the profound metamorphosis that machine learning has ushered into the realm of drug discovery, underscoring its pivotal role in revolutionizing, and reshaping the contours of pharmaceutical research.en-US
dc.formatapplication/pdf
dc.identifierhttps://journals.researchparks.org/index.php/IJOT/article/view/4725
dc.identifier.urihttp://dspace.umsida.ac.id/handle/123456789/16754
dc.languageeng
dc.publisherResearch Parks Publishing LLCen-US
dc.relationhttps://journals.researchparks.org/index.php/IJOT/article/view/4725/4399
dc.rightsCopyright (c) 2023 International Journal on Orange Technologiesen-US
dc.sourceInternational Journal on Orange Technologies; Vol. 5 No. 8 (2023): International Journal on Orange Technologies; 29-37en-US
dc.source2615-8140
dc.source2615-7071
dc.subjectMachine Learningen-US
dc.subjectDrug Discoveryen-US
dc.subjectCloud Computingen-US
dc.subjectAdvance Applicationsen-US
dc.titleMachine Learning in Drug Discovery: A Comprehensive Analysis of Applications, Challenges, and Future Directionsen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typePeer-reviewed Articleen-US
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