Revolutionizing Pharmaceutical Research: Harnessing Machine Learning for a Paradigm Shift in Drug Discovery - Ali Husnain; Saad Rasool; Ayesha Saeed; Hafiz Khawar Hussain (Information Technology and Science (ITScience), 2023)
The fusion of machine learning (ML) and artificial intelligence (AI) is experiencing a dramatic transition in the field of pharmaceutical research and development. This study examines the several effects of machine learning (ML) on different phases of medication discovery, development, and patient care. The capability of ML to quickly process huge chemical libraries and forecast interactions with target proteins is studied, starting with compound screening and selection. The potential for fewer false positives and negatives, improved hit prediction accuracy, and ensemble technique use are underlined. The part that machine learning plays in enhancing Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile is then explained. ML models anticipate compound actions inside the human body by analyzing molecular structures and characteristics, improving assessments of drug safety and efficacy. The article goes into further detail about predictive modeling, highlighting how machine learning may be used to find prospective therapeutic targets and confirm their applicability. The combination of multi-omics data, deep learning, and the possibility to identify similar molecular pathways across diseases highlight the game-changing potential of machine learning in this field. The article also covers the use of ML in clinical trials, highlighting its benefits for trial planning, patient recruitment, real-time monitoring, and individualized therapy predictions. By utilizing computational analysis and quantum physics, the power of machine learning-driven de novo drug creation is examined, revealing the potential to develop new therapeutic candidates. In this article, the ethical issues surrounding AI-driven drug discovery are discussed, with a focus on the necessity of transparent data utilization, human oversight, and responsible data consumption. The report ends by predicting ML's potential for pharmaceutical R&D in the future. Accelerated drug discovery pipelines, the rise of customized medicine powered by predictive models, optimized clinical trials, and a change in medication repurposing tactics are all envisaged in this. The report emphasizes the revolutionary potential of ML in altering pharmaceutical research and development while noting obstacles in data quality, model interpretability, ethics, and interdisciplinary collaboration. It is suggested that the ethical integration of AI technologies, interdisciplinary cooperation, and regulatory modifications are essential steps to unlock the full potential of ML and AI and, ultimately, provide patients throughout the world with safer, more efficient, and individualized treatments.