Artificial Intelligence and Machine Learning in Drug Discovery and Development

2. Ai In Drug Discovery And Development

Methods such as molecular docking, quantum mechanics, and statistical learning are utilized in the process of mining chemical databases for the discovery of novel inhibitors. Both strategies have seen significant use in the process of drug development, with the end goal of locating potentially useful lead molecules. This field of research is also known as computational drug design, computer-aided molecular design, computer-aided molecular modelling, rational drug design, in silico drug design, and computer-aided rational drug design. In silico drug, design refers to the process of designing drugs entirely within a computer simulation environment. All of these terms refer to the same thing: designing drugs using computers. During the entirety of this investigation, the term “computer-assisted drug discovery and development” (CADDD) will be utilized to refer to the overall process. It is not impossible to combine computational and experimental methodologies. One of the most important and crucial areas in which artificial intelligence have shown to be of the utmost significance is the field of drug research and development. In today's world, when more and more diseases are posing a threat to people's lives and there is an ever-increasing demand for more and more medicines to combat these diseases, there is an ever-increasing need to identify innovative therapeutic molecules at a faster rate and with greater precision. This is because the number of diseases that threaten people's lives is also increasing. It is possible that the data sets given to pharmaceutical businesses for the purpose of medical development contain millions of molecules; hence, it is impossible for a typical machine-learning system to process these molecules. As a result, the process of drug discovery and development calls for increasingly sophisticated AI systems (Yang & Siau, 2018).