AI Based Optimization of Fault Tolerance in Multipath Routing for Medical Application

Artificial Intelligence (AI) has had a substantial influence on the process of pharmaceutical research and development over the course of the last decade. Several applications that are connected to drug discovery, such as virtual screening and drug design, have made use of a number of AI's varied approaches. One of these applications is drug discovery. In the first section of this study, we are going to present an overview of drug discovery and discuss applications that are relevant to this field. These applications may be divided into two major tasks, which are synthesizing new compounds and making predictions about the characteristics of existing molecules. After that, this study will discuss about the standard data resources, different representations of molecules, and benchmark platforms. As a crucial component of the investigation, the methodologies of artificial intelligence are dissected into a variety of model architectures and learning paradigms. The reviewed papers have been arranged in a chronological manner to demonstrate the development of the technical features of AI in drug discovery throughout the course of time. According to our anticipations, the results of this study should provide a comprehensive analysis of the use of AI in drug development. An additional instructional resource that we make accessible to users is a repository that can be found on GitHub and is comprised of a variety of publications (and codes, if relevant). This archival storage space is regularly upgraded.