Abstract
Fragment-based drug or lead discovery (FBDD or FBLD) refers to as one of
the most significant approaches in the domain of current research in the pharmaceutical
industry as well as academia. It offers a number of advantages compared to the
conventional drug discovery approach, which include – 1) It needs the lesser size of
chemical databases for the development of fragments, 2) A wide spectrum of
biophysical methodologies can be utilized for the selection of the best fit fragments
against a particular receptor, and 3) It is far more simpler, feasible, and scalable in
terms of the application when compared to the classical high-throughput screening
methods, making it more popular day by day. For a fragment to become a drug
candidate, they are analyzed and evaluated on the basis of numerous strategies and
criteria, which are thoroughly explained in this chapter. One important term in the field
of FBDD is de novo drug design (DNDD), which means the design and development of
new ligand molecules or drug candidates from scratch using a wide range of in silico
approaches and algorithmic tools, among which AI-based platforms are gaining large
attraction. A principle segment of AI includes DRL that finds numerous applicabilities
in the DNDD sector, such as the discovery of novel inhibitors of BACE1 enzyme,
identification and optimization of new antagonists of DDR1 kinase enzyme, and
development and design of ligand molecules specific to target adenosine A2A, etc. In
this book chapter, several aspects of both FBDD and DNDD are briefly discussed.