Artificial Intelligence In Drug Discovery And Development - Mbuso Mabuza

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Résumé :
Artificial intelligence (AI) is a simulation of the process of human intelligence through computers. AI has cemented its status as a powerful technology with the ability to propel a paradigm shift in healthcare and medicine of the 21st century and the future. The insights and values gained from AI and its subset, machine learning, are essential for predicting health outcomes and improving decision-making in healthcare and medicine. AI can offer revolutionary insights into medicine, through data from genetics, proteomics and other life sciences that advance the process of drug discovery and development. Discovering drugs is a crucial first step in the biopharmaceutical value chain. Drug discovery is a long, expensive and often unsuccessful process. The biopharmaceutical industry makes efforts to employ AI to improve drug discovery, reduce research and development costs, reduce the time and cost of early drug discovery, and support predicting potential risks/side effects in late clinical trials that can be useful in avoiding traumatic events in clinical trials. The rapid growth in life sciences and machine learning algorithms has led to enormous statistical access to the growth of AI-based start-ups focused on drug innovation in recent years. The growing need to curb drug discovery costs and reduce time involved in the drug development process, the rising adoption of cloud-based applications and services, and the impending patent expiry of blockbuster drugs are some of the key factors driving the growth of this market. However, shortage of AI workforce and ambiguous regulatory guidelines for medical software and lack of data sets in this field are some of the factors expected to restrain the growth of this market in the coming years.

Biographie:
Highly motivated life-long learner, humanitarian, physician, bio-chemist, international public health expert, occupational health expert, business administration expert, business project management expert, researcher, writer, entrepreneur, innovative individual, growth strategist, life coach and human potential enabler with more than ten years of experience leading demanding health and business portfolios at organisational and national level in the public and private sectors of different countries....

Sommaire:
Artificial Intelligence (AI) represents the simulation of human intelligence processes by computer systems, and in the 21st century, it has emerged as a transformative force in the healthcare and medical sectors. By leveraging machine learning-a key subset of AI-researchers and clinicians can extract vital insights from vast datasets, enabling more accurate predictions of health outcomes and significantly enhancing clinical decision-making. One of the most revolutionary applications of AI is found within the biopharmaceutical value chain, specifically in the field of drug discovery and development. Traditionally, discovering new medications is an arduous, high-risk, and prohibitively expensive process, often spanning over a decade with high failure rates. AI addresses these challenges by analyzing complex biological data from genetics, proteomics, and other life sciences to identify promising drug candidates more efficiently. The integration of AI into the drug discovery pipeline offers several critical advantages:Cost and Time Reduction: AI streamlines research and development, lowering the financial barriers to early-stage discovery. Enhanced Safety: By predicting potential risks and side effects before drugs reach human participants, AI helps avoid traumatic events during late-stage clinical trials. Innovation Growth: There has been a surge in AI-based startups focused on drug innovation, driven by the increasing availability of sophisticated algorithms and the rising adoption of cloud-based services. Market Pressures: The impending patent expiration of various blockbuster drugs is pushing the industry to accelerate innovation through automated intelligence. Despite its immense potential, the field faces notable hurdles. The growth of the AI-driven healthcare market is currently restrained by a significant shortage of specialized workers, ambiguous regulatory guidelines for medical-grade software, and a lack of high-quality, standardized datasets. Overcoming these barriers will be essential for AI to fully realize its role in ushering in a new era of precision medicine and pharmaceutical efficiency....