Recent Advancements in Computing
Reliable Binding Free Energies
in Drug Discovery Projects
N. Arul Murugan, Vasanthanathan Poongavanam
and U. Deva Priyakumar
Abstract In recent times, our healthcare system is being challenged by many
drug-resistant microorganisms and ageing-associated diseases for which we do not
have any drugs or drugs with poor therapeutic profile. With pharmaceutical technological advancements, increasing computational power and growth of related
biomedical fields, there have been dramatic increase in the number of drugs approved in
general, but still way behind in drug discovery for certain class of diseases. Now, we
have access to bigger genomics database, better biophysical methods, and knowledge
about chemical space with which we should be able to easily explore and predict
synthetically feasible compounds for the lead optimization process. In this chapter, we
discuss the limitations and highlights of currently available computational methods
used for protein–ligand binding affinities estimation and this includes force-field,
ab initio electronic structure theory and machine learning approaches. Since the electronic structure-based approach cannot be applied to systems of larger length scale, the
free energy methods based on this employ certain approximations, and these have been
discussed in detail in this chapter. Recently, the methods based on electronic structure
theory and machine learning approaches also are successfully being used to compute
protein–ligand binding affinities and other pharmacokinetic and pharmacodynamic
properties and so have greater potential to take forward computer-aided drug discovery
to newer heights.
N. A. Murugan (&)
Department of Theoretical Chemistry and Biology, School of Engineering
Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology,
Stockholm, Sweden
e-mail: murugan@kth.se
V. Poongavanam
Department of Physics, Chemistry, Pharmacy, University of Southern Denmark,
Campusvej 55, DK-5230 Odense M, Denmark
U. D. Priyakumar
CCNSB, International Institute of Information Technology,
Gachibowli 500032, Hyderabad, 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_7
221
Reliable Binding Free Energies
in Drug Discovery Projects
N. Arul Murugan, Vasanthanathan Poongavanam
and U. Deva Priyakumar
Abstract In recent times, our healthcare system is being challenged by many
drug-resistant microorganisms and ageing-associated diseases for which we do not
have any drugs or drugs with poor therapeutic profile. With pharmaceutical technological advancements, increasing computational power and growth of related
biomedical fields, there have been dramatic increase in the number of drugs approved in
general, but still way behind in drug discovery for certain class of diseases. Now, we
have access to bigger genomics database, better biophysical methods, and knowledge
about chemical space with which we should be able to easily explore and predict
synthetically feasible compounds for the lead optimization process. In this chapter, we
discuss the limitations and highlights of currently available computational methods
used for protein–ligand binding affinities estimation and this includes force-field,
ab initio electronic structure theory and machine learning approaches. Since the electronic structure-based approach cannot be applied to systems of larger length scale, the
free energy methods based on this employ certain approximations, and these have been
discussed in detail in this chapter. Recently, the methods based on electronic structure
theory and machine learning approaches also are successfully being used to compute
protein–ligand binding affinities and other pharmacokinetic and pharmacodynamic
properties and so have greater potential to take forward computer-aided drug discovery
to newer heights.
N. A. Murugan (&)
Department of Theoretical Chemistry and Biology, School of Engineering
Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology,
Stockholm, Sweden
e-mail: murugan@kth.se
V. Poongavanam
Department of Physics, Chemistry, Pharmacy, University of Southern Denmark,
Campusvej 55, DK-5230 Odense M, Denmark
U. D. Priyakumar
CCNSB, International Institute of Information Technology,
Gachibowli 500032, Hyderabad, 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_7
221