Introduction: Structure-based drug design is a wide area of identification of selective inhibitors
of a target of interest. From the time of the availability of three dimensional structure of the drug
targets, mostly the proteins, many computational methods had emerged to address the challenges associated
with drug design process. Particularly, drug-likeness, druggability of the target protein, specificity,
off-target binding, etc., are the important factors to determine the efficacy of new chemical inhibitors.
Objective: The aim of the present research was to improve the drug design strategies in field of design
of novel inhibitors with respect to specific target protein in disease pathology. Recent statistical machine
learning methods applied for structural and chemical data analysis had been elaborated in current drug
Methods: As the size of the biological data shows a continuous growth, new computational algorithms
and analytical methods are being developed with different objectives. It covers a wide area, from protein
structure prediction to drug toxicity prediction. Moreover, the computational methods are available to
analyze the structural data of varying types and sizes of which, most of the semi-empirical force field
and quantum mechanics based molecular modeling methods showed a proven accuracy towards analysing
small structural data sets while statistics based methods such as machine learning, QSAR and other
specific data analytics methods are robust for large scale data analysis.
Results: In this present study, the background has been reviewed for new drug lead development with
respect specific drug targets of interest. Overall approach of both the extreme methods were also used to
demonstrate with the plausible outcome.
Conclusion: In this chapter, we focus on the recent developments in the structure-based drug design
using advanced molecular modeling techniques in conjunction with machine learning and other data
analytics methods. Natural products based drug discovery is also discussed.