Abstract
The continuous implementation of Artificial Intelligence (AI) in multiple
scientific domains and the rapid advancement in computer software and hardware,
along with other parameters, have rapidly fuelled this development. The
technology can contribute effectively in solving many challenges and constraints
in the traditional development of the drug. Traditionally, large-scale chemical
libraries are screened to find one promising medicine. In recent years, more
reasonable structure-based drug design approaches have avoided the first
screening phases while still requiring chemists to design, synthesize, and test
a wide range of compounds to produce possible novel medications. The process of
turning a promising chemical into a medicinal candidate can be expensive and
time-consuming. Additionally, a new medication candidate may still fail in
clinical trials even after demonstrating promise in laboratory research. In
fact, less than 10% of medication candidates that undergo Phase I trials
really reach the market. As a consequence, the unmatched data processing power
of AI systems may expedite and enhance the drug development process in four
different ways: by opening up links to novel biological systems, superior or
distinctive chemistry, greater success rates, and faster and less expensive
innovation trials. Since these technologies may be used to address a variety of
discovery scenarios and biological targets, it is essential to comprehend and
distinguish between use cases. As a result, we have emphasized how AI may be
used in a variety of areas of the pharmaceutical sciences, including in-depth
opportunities for drug research and development.
Key words
artificial intelligence - artificial neural networks - computer-aided drug design - deep learning - drug design and discovery - machine learning - quantitative structure-activity relationship
