Structure-Based Drug Design
with a Special Emphasis on Herbal
Extracts
D. Velmurugan, N. H. V. Kutumbarao, V. Viswanathan
and Atanu Bhattacharjee
Abstract Structure-based drug design (SBDD) is a computational analysis of
identifying ligands which can potentially inhibit the target. SBDD is a cluster of
methods and modules which reduces the cost and time spent on experimental
procedures. SBDD plays a crucial role in preclinical drug development procedures.
There is a vast development in techniques and methods related to theoretical
physics and chemistry, computers processers, and pharmacokinetic analysis which
helps in elucidating the biological role of ligands and their receptors. Here, the
general theoretical backgrounds of various SBDD and simulation approaches
employed are discussed. These methods are also discussed with respect to the
identification of potential drug-like molecules from natural sources to control
human ailments.
Keywords Docking Á Molecular simulations Á Pharmacophore
Force field Á Crystallography Á Natural products
1 Introduction
Drug discovery involves computation in major ways. Structure-based methods
involve discovery of lead compounds, their refinement, and re-engineering to
overcome resistance. As the number of protein structures available in the Protein
Data Bank (PDB) has crossed 1.3 lakhs, SBDD effort with potent targets has
progressed well. Compounder model uses many advances in the visualization of
molecular structures. Insight II, Quanta, Cerius 2 [1], Sybyl [2], and CAChe [3] are
D. Velmurugan (&) Á N. H. V. Kutumbarao Á V. Viswanathan
CAS in Crystallography and Biophysics, University of Madras,
Guindy Campus, Chennai 600025, Tamil Nadu, India
e-mail: shirai2011@gmail.com
A. Bhattacharjee
Department of Biotechnology & Bioinformatics, North-Eastern
Hill University, Umshing Mawkynroh, Shillong 793022, India
© Springer Nature Switzerland AG 2019
C. G. Mohan (ed.), Structural Bioinformatics: Applications in Preclinical Drug
Discovery Process, Challenges and Advances in Computational Chemistry
and Physics 27, https://doi.org/10.1007/978-3-030-05282-9_9
271
with a Special Emphasis on Herbal
Extracts
D. Velmurugan, N. H. V. Kutumbarao, V. Viswanathan
and Atanu Bhattacharjee
Abstract Structure-based drug design (SBDD) is a computational analysis of
identifying ligands which can potentially inhibit the target. SBDD is a cluster of
methods and modules which reduces the cost and time spent on experimental
procedures. SBDD plays a crucial role in preclinical drug development procedures.
There is a vast development in techniques and methods related to theoretical
physics and chemistry, computers processers, and pharmacokinetic analysis which
helps in elucidating the biological role of ligands and their receptors. Here, the
general theoretical backgrounds of various SBDD and simulation approaches
employed are discussed. These methods are also discussed with respect to the
identification of potential drug-like molecules from natural sources to control
human ailments.
Keywords Docking Á Molecular simulations Á Pharmacophore
Force field Á Crystallography Á Natural products
1 Introduction
Drug discovery involves computation in major ways. Structure-based methods
involve discovery of lead compounds, their refinement, and re-engineering to
overcome resistance. As the number of protein structures available in the Protein
Data Bank (PDB) has crossed 1.3 lakhs, SBDD effort with potent targets has
progressed well. Compounder model uses many advances in the visualization of
molecular structures. Insight II, Quanta, Cerius 2 [1], Sybyl [2], and CAChe [3] are
D. Velmurugan (&) Á N. H. V. Kutumbarao Á V. Viswanathan
CAS in Crystallography and Biophysics, University of Madras,
Guindy Campus, Chennai 600025, Tamil Nadu, India
e-mail: shirai2011@gmail.com
A. Bhattacharjee
Department of Biotechnology & Bioinformatics, North-Eastern
Hill University, Umshing Mawkynroh, Shillong 793022, India
© Springer Nature Switzerland AG 2019
C. G. Mohan (ed.), Structural Bioinformatics: Applications in Preclinical Drug
Discovery Process, Challenges and Advances in Computational Chemistry
and Physics 27, https://doi.org/10.1007/978-3-030-05282-9_9
271