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Recent Advancements in Computing Reliable Binding Free Energies in Drug Discovery Projects

  • N. Arul MuruganEmail author
  • Vasanthanathan Poongavanam
  • U. Deva Priyakumar
Chapter
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 27)

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.

Keywords

Computational drug discovery Free energy of binding Hybrid QM/MM QM fragmentation Binding affinity Pharmacokinetic (PK) properties Machine learning approach 

Abbreviations

FMO

Fragment molecular orbital

MAO-B

Monoamine oxidase B

MM-GBSA

Molecular mechanics–Generalized Born Surface Area

MM-PBSA

Molecular mechanics–Poisson–Boltzmann Surface Area

PD

Pharmacodynamic

PK

Pharmacokinetic

QM/MM

Quantum mechanics/molecular mechanics

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. Arul Murugan
    • 1
    Email author
  • Vasanthanathan Poongavanam
    • 2
  • U. Deva Priyakumar
    • 3
  1. 1.Department of Theoretical Chemistry and Biology, School of Engineering Sciences in Chemistry, Biotechnology and HealthRoyal Institute of TechnologyStockholmSweden
  2. 2.Department of Physics, Chemistry, PharmacyUniversity of Southern DenmarkOdense MDenmark
  3. 3.CCNSB, International Institute of Information TechnologyGachibowliIndia

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