Revolutionizing Drug Discovery; Transformative Role of Machine Learning

Moazzam Siddiq (Independent Researcher, Manchester, United Kingdom.)

BULLET : Jurnal Multidisiplin Ilmu
Vol. 1 No. 02 (2022): BULLET : Jurnal Multidisiplin Ilmu

The use of machine learning in drug discovery is examined in this review article along with any potential advantages, difficulties, and prospective future developments. The article examines the many machine learning models that have been created for these uses and emphasises the value of machine learning in predicting drug characteristics, discovering new therapeutic targets, and creating new drug candidates. The need for high-quality data, increased collaboration and data sharing, as well as ethical and regulatory considerations, are just a few of the obstacles and limitations of employing machine learning in drug discovery that are covered in this article. The study also highlights the necessity of regulatory frameworks that can guarantee the safety and efficacy of novel pharmaceuticals generated using these models, as well as the significance of transparency and accountability in the usage of machine learning algorithms. The discussion of potential future paths and prospects for development in the field of machine learning in drug discovery finishes the essay. Deep learning models, multi-task learning, personalised medicine, and the fusion of machine learning with other technologies like robotics and automation are a few examples of these. In order to speed up the drug discovery process and provide novel, efficient medicines to patients in need, the authors propose tackling the difficulties and limitations of machine learning in drug discovery as well as continuing to investigate these exciting areas of research and development. This review paper offers a thorough summary of the current status of machine learning in drug discovery, stressing its potential advantages and disadvantages as well as outlining the major areas for future research and development that are expected to spur advancement. Researchers, medication developers, and politicians who are curious about how machine learning could change the drug discovery process and enhance patient outcomes will find the paper interesting.