| | July 20199· AI for Analyzing Research Lit-erature, Publications, & PatentsAI-driven supercomputer for accelerating analysis and tests of hypothesis by researchers using `massive volumes of disparate data sources' that include million sourc-es of laboratory data reports as well as medical literature. AI is helpful for scientific data mining, data con-textualization and deriving Hypoth-esis. Navigating through all this information to draw meaningful insights about drug candidates is where AI-based algorithms become indispensable.· AI in Drug Discovery & DevelopmentApplication of big data analytics and machine learning-based AI for drug development thus augment hu-man intelligence for analyzing data and identifying new patterns and insights. It recognizes protein and ligand structures and interactions. Further, it can model bioactivity of small molecules and chemical in-teractions and identify new mole-cules for the targets with previously unknown modulators. Deep-learning screening of bio-markers includes genome, proteome, metabolome, and lipidome data of the biological samples to unravel the complex biological networks playing roles in diseases and help identify medications for specific patient pop-ulations. AI tool is used to identify molecular signatures and potential biomarkers for assessing the vaccine immunological response.Computational physics and artifi-cial intelligence for accurate molecu-lar modeling of drug-like small mol-ecules. Neural network application is being applied for structure-based drug design and discovery in which researcher uses the application by virtually combining atoms to come up with possible molecules. Deep Genomics, a Toronto com-pany, looks for patterns in genomic data to find causal relationships with specific diseases. BERG Health is using AI to analyze tissue samples, genomics, and other data pertinent to a disease, which has resulted in a potential new drug for topical squa-mous-cell carcinoma. · AI in Drug ManufacturingThrough integration with AI self-learning machines, the complex operations in pharma manufacturing plants can be simplified to a great-er degree. While this will certain-ly reduce the time and increase the efficiency, it will also enhance the reporting quality. These developing technologies will enable operations to become in-telligent and efficient, although they pose a challenge for the policy mak-ers and regulators to re-define the way we understand the current Good Manufacturing Practices (cGMP). AI-enabled machines will em-phasize a means of rejecting the root-cause(s) for the output going `Out-of-Specification'. Understand-ing the logics and patterns, predict-ing the variations and adjusting the process beforehand will preempt un-necessary product failures. This will also minimize any redundancy in the process, improve the yield, ensure consistency and stabilize quality and ultimately exhibit complete regula-tory compliance resulting in a prod-uct fulfilling its critical quality attri-butes. Thus, the expectations of the regulatory agencies in regards Con-tinuous Quality Verification (CQV) will evidently be satisfied by AI and its applications through machine learning tools. · AI in Clinical Trial ManagementArtificial intelligence (AI) tech-nology, combined with big data, hold the potential to solve many key clini-cal trial challenges with a better pro-tocol design, patient enrolment and retention, and study start-up as prime areas for improvement. Technologies such as digital re-porting apps, as well as wearables, allow for real-time engagement and communication, and support pa-tient-centric trials. Patients can send feedback on treatment symptoms and manage medication intake, and can share information with researchers with more meaningful clinically rel-evant insights and be used to assess and develop trial objectives, end-points and procedures, reducing or eliminating the need for patients to travel to sites, which increases pa-tient adherence and compliance. AI analysis of live remote data also can With Artificial Intelligence, there has been a shift in this trend because reactive medical care became proactive medical care
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