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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.

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While some heavy metals are essential trace components, many are bio toxic in human biochemistry. As a result, a full grasp of underlying systems is required. For supporting life and minimizing environmental damage, we must grasp their sources, liquidation techniques, chemical modifications, and deposition patterns These metals are discharged into the environment by both natural and human activity, including mining, industrial operations, and vehicular emissions. Soils and groundwater are contaminated when they leak into subsurface fluids and eventually into the aquifer. The world's commerce and coordinating systems frequently generate environmental toxicity and poisoning. Swallowed, they generate bio toxic compounds, lose structural integrity, and block bio reactions. This study's goal is to learn more about heavy metals and their bio toxic effects on humans.