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All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu
Moazzam Siddiq
Independent Researcher, Manchester, United Kingdom.
Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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Revolutionizing Drug Discovery; Transformative Role of Machine Learning Moazzam Siddiq
BULLET : Jurnal Multidisiplin Ilmu Vol. 1 No. 02 (2022): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

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.
Use of Machine Learning to predict patient developing a disease or condition for early diagnose Moazzam Siddiq
International Journal of Multidisciplinary Sciences and Arts Vol. 1 No. 1 (2022): International Journal of Multidisciplinary Sciences and Arts, Article August 20
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v1i1.2271

Abstract

Machine learning algorithms have shown promise in predicting the likelihood of a patient developing a disease or condition. Early diagnosis of diseases such as cancer, diabetes, and cardiovascular diseases can improve the patient's outcomes and quality of life. In this paper, we review the current state of machine learning algorithms for disease prediction and discuss their potential applications in clinical practice. We start by discussing the types of data used for disease prediction, including clinical data, genetic data, and imaging data. We then review the different types of machine learning algorithms used for disease prediction, including logistic regression, decision trees, random forests, and deep learning. We discuss the advantages and limitations of each algorithm and provide examples of their applications in disease prediction. Next, we discuss the challenges associated with implementing machine learning algorithms in clinical practice, such as data privacy concerns and the need for high-quality data. We also discuss the ethical considerations associated with the use of machine learning algorithms for disease prediction. Finally, we highlight the potential benefits of using machine learning algorithms for disease prediction, including improved patient outcomes, reduced healthcare costs, and personalized medicine. We conclude that machine learning algorithms have the potential to revolutionize disease prediction and early diagnosis, but further research is needed to address the challenges associated with their implementation in clinical practice.
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