ABSTRACT
In a variety of areas, deep learning techniques have achieved state-of-the-art performance. In the medical profession, deep learning is becoming increasingly relevant. It has predictive behaviors that allow it to evaluate complex medical data accurately. It is critical for healthcare providers to grasp deep learning potential and limitations as it is being integrated into many disciplines of medicine. Deep learning has become increasingly important in the pharmaceutical business in recent years, particularly in drug discovery and drug interaction prediction. It can be used to screen and discover potential chemicals. Deep learning is sophisticated enough to identify feasible medication combinations from millions of chemical components. Deep learning plays an important role in discovering medication combinations. During the pandemic, disruptive technologies such as artificial intelligence (AI), machine learning, and deep learning were used to fund vaccine along with medication development. Because drug discovery is a complicated endeavor, deep learning can make it more efficient, cost-effective, and simple. Deep learning algorithms can anticipate pharmacological characteristics, forecast drug-target interactions, and generate compounds with desired properties. Deep learning algorithms can process genetic, clinical, and population data with ease, and a variety of toolkits can be used to find patterns in the data. Researchers are now able to undertake faster molecular modelling and predictive analytics in identifying protein structures by utilizing machine learning and deep learning. This chapter mainly discusses application of deep learning in the healthcare industry.
