How Machine Learning Models Can Revolutionize Pharma and Healthcare (original) (raw)
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Investigating the Impact of Machine Learning in Pharmaceutical Industry
Journal of Pharmaceutical Research International
In the pharmaceutical and consumer health industries, artificial intelligence and machine learning played an important role. These technologies are critical for the identification of patients with improved intelligence applications, such as disease detection and diagnostics for clinical testing, for medicine production and predictive forecasts. In recent years, advances in numerous analysis tools and machine learning algorithms have led to novel applications for machine learning in several areas of pharmaceutical science. This paper examines the past, present, and future impacts of machine learning on several areas, including medicine design and discovery. Artificial neural networks are employed in pharmaceutical machine learning because they can reproduce nonlinear interactions typical in pharmaceutical research. AI and learning machines are examined in everyday pharmaceutical needs, industrial and regulatory insights.
2020
Over the past few years, pharmaceutical R&D has become aware of the potential benefits of leveraging artificial intelligence and its collective subfields including machine learning, deep learning, data science and advanced analytics. These technologies are being embraced across industries to provide enhanced automation, gain insights into data, and improve data-driven decision making. The evangelization from lower level technical experts has now been echoed by the top levels of many organizations, as exemplified by Vas Narasimhan’s (Novartis CEO) goal to evolve AI to place it at the “heart of the company” [1] and Alex Bourla’s (Pfizer CEO) aim to win the digital race in pharma using machine learning and AI to expedite R&D [2]. Although its value compared to pure science continues to be questioned, machine learning and particularly deep learning have introduced many compelling use cases.
International Journal of Multidisciplinary Sciences and Arts, 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.
Artificial Intelligence in Pharmacy Innovations, Applications, and Future Emerging Challenges
IJSREM, 2024
AI is transforming the world of pharmaceuticals, spurring innovation in areas like drug discovery, drug development, drug manufacturing, microbiology and personalized medicine. Artificial intelligence (AI) is changing the way drugs are identified, provided, and optimized to ensure patient safety by utilizing advanced technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP). This aids in the faster, accurate identification of drug candidates, enhances clinical trials, and leads to improved therapeutic pipelines by facilitating precise and efficient data analysis. AI helps speed up drug discovery through analyzing vast datasets to also predict molecular interactions and possible side effects. In microbiology, AI is contributing both to identifying antimicrobials and tackling the challenges presented by antimicrobial resistance (AMR). AI applications on patient-data (such as genetic information, lifestyle choice, and clinical histories) can contribute in delivering personalized medicine that enhances the efficacy of treatment by providing information for exposures and reducing negative outcomes. In addition, it strengthens pharmacovigilance initiatives through real-time analysis of drug safety in the post-market space, early detection of ADRs, and risk assessment to address safety concerns for patients. AI has also been a game-changer in various pharmaceutical manufacturing areas where AI accelerates the production process and improves product uniformity. Artificial intelligence-based algorithms are maximizing automation, making supply chains more efficient, and reinforcing quality management systems. AI can track manufacturing processes in real time by examining data from sensors and equipment, allowing it to identify and address problems before they arise, leading to more efficient and manufacturing processes. However, there are still substantial barriers to the widespread adoption of AI in healthcare. One pressing issue is how to tailor AI technologies to healthcare systems that, in general, have quality and standardized datasets to provide the best possible outcomes. Challenges associated with data privacy, regulatory frameworks, and the need for transparent and interpretable AI models persist. The obstacles remain, but the potential to transform drug development, manufacturing, and patient care is immense with AI. This review discusses the expected impacts of AI in the pharmaceutical practices and the challenges ahead to be able to unlock these benefits from AI in a health-care setting.
Artificial Intelligence in Pharmaceutical and Healthcare Research
Big Data and Cognitive Computing
Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the poten...
APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL INDUSTRY AND HEALTH CARE SECTOR
Shodhasamhita: Journal of Fundamental & Comparative Research, 2022
Artificial intelligence is the upcoming technology in advance health care system. Current digitalization of medicine and availability of electronic health records (EHRs) has inspired clinical researchers and healthcare personnel to acquire artificial intelligence (AI) methodologies for big data analytics and for very large scale medical databases. The application of machine learning helps to interpret and understand numbers of electronic health records(EHRs) (e.g., pharmacokinetic consultations, patients' medication history, medication safety, medication errors, adverse drug reactions, interactions, and therapeutic outcomes) become a required appliance to perform realtime evaluations of the safety, efficacy, to improve patient care and relative efficacy of available drugs. Artificial intelligence & Robots play vital role to achieve consistent quality product, to increase productivity, & to remove more work load from workers. Current scenario of AI in the field of pharmacy are CFD, automated Pharmaceutical development & research which give complete information related with healthcare system. AI with robotics has many advantages & disadvantages. Different AI techniques such as machine learning, ANN, AI in clinical practice, AI in healthcare system, are increasingly used nowadays. It is always difficult to tell about the future, but it will become possible by AI, and it will be the future aspects in the health care system.
Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development
International Journal of Molecular Sciences
The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various ...
Machine Learning used in the field of Pharmacy
International Journal of Advances in Computer Science and Technology , 2024
The application of intelligence in technology is expanding to include machine-prevalent methods. It could reduce expenses and save time, all the while additionally enhancing our comprehension of how different formulations and process parameters interact. Artificial intelligence, which falls under the realm of computer science is concentrated on problem-solving through programming. It has evolved into a science of problem solving with applications in industries like technology, medicine and more. The research paper covers a range of topics such as discovering peptides from sources, managing and treating rare diseases ensuring proper drug adherence and dosage as well as discussing barriers, to implementing AI in the pharmaceutical industry. It also touches upon automated control procedures, manufacturing execution systems and using AI for treatment predictions.
Machine Learning and Artificial Intelligence Strategies for the Pharmaceutical Industry
DBA, 2022
The quantitative, correlational study investigated factors that influence the initiation, convergence opportunity, highest return on investment, and variables that can delay machine learning and artificial intelligence in the pharmaceutical industry. The study population constituted individuals from the United States Food and Drug Administration- pharmaceutical sites registry of 2018, N = 4,676 representing all sectors of the pharmaceutical industry. The hypothesis testing used a sample size of N = 76. The samples were qualified for analysis based on codifying questions 14, and 19. The hypothesis tests applied α level of .05. The first hypothesis utilizing Kruskal Walli test confirmed a statistically significant difference in natural language processing use study survey responses across the preferred factors. The p-values of complexity and availability factors are less than the significance level of .05, indicating that the median number of at least one factor differs, rejecting H10. The second hypothesis test with Kendall’s τb correlation method identified machine learning and artificial intelligence convergence in the future τ = .371, p < .001, rejecting H20. Spearman’s rank-order correlation test on the third hypothesis confirmed a moderate-to-weak positive statistical correlation between natural language processing use study responses and level of return on investment, rejecting H30. The fourth hypothesis applying Spearman’s rank-order correlation test confirmed a strong-toweak statistical relationship between natural language processing use study responses and respondents’ outlook on implementation delay factors, rejecting H40.
ACCELERATING PHARMACEUTICAL INNOVATION: THE IMPACT OF AI ON DRUG DISCOVERY
IAEME PUBLICATION, 2024
A/B testing has emerged as a powerful tool for optimizing and fine-tuning machine learning (ML) models, enabling researchers and practitioners to make data-driven decisions based on empirical evidence. This article provides a comprehensive overview of the application of A/B testing in the context of ML model optimization, covering key concepts, methodologies, and best practices. We explore the fundamentals of ML models, the A/B testing process, and the formulation of effective testing hypotheses. The article also discusses common pitfalls and challenges associated with A/B testing experiments, emphasizing the importance of well-constructed experiments, appropriate sample sizes, and the selection of suitable evaluation metrics. Furthermore, we delve into the role of user segmentation in A/B testing, highlighting its significance in understanding the impact of proposed changes on different user groups. The article concludes by emphasizing the need for a rigorous and systematic approach to A/B testing in ML model optimization, underlining the potential of this technique to advance the field of AI and deliver more accurate, reliable, and impactful ML solutions. Through a comprehensive review of the literature and the inclusion of relevant examples, this article serves as a valuable resource for researchers and practitioners seeking to harness the power of A/B testing in their ML projects.