Table of Contents
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PREFACE
List of Contributors
Computer-Aided Molecular Design in Computational Chemistry
Abstract
Introduction
Methods
Markovian Chemicals “in silico” Design (MARCH INSIDE)
Methodology
Statistical Analysis using MATCH-INSIDE
Iso-Contribution Zone Analysis (IZA)
DENSITY FUNCTIONAL THEORY (DFT)
Geometry Optimization
Spectroscopical Analysis
2.2.1. UV- Vis analysis
FT-IR Analysis
NMR Analysis
Non-Linear Optical (NLO) Analysis
COSMO-CAMD: Optimization Methods Based on Computer-Aided Molecular Design using COSMO-RS
Framework of COSMO-CAMD
Ab Initio Method
Born-Oppenheimer Approximation
Recent Developments in the Ab Initio Method
Ab Initio Crystal Field for Lanthanides
Hartree-Fock Method
Ab Initio Nonreactors
Group Contribution Method
Continuous Molecular Targeting (CoMT−CAMD)
2nd step: Mapping Step for Best-Performing Components Identification
Conclusion
CONSENT FOR PUBLICATION
Conflict of Interest
Acknowlegments
References
Role of Ensemble Conformational Sampling Using Molecular Docking & Dynamics in Drug Discovery
Abstract
INTRODUCTION
Drug and Drug Designing
Computer-Aided Drug Discovery (CADD)
MOLECULAR DOCKING
Sampling Methods for Docking
Rigid Docking Approach
Semi-Flexible Docking Approach
Systematic Search Techniques
Stochastic Methods
Flexible Docking Approach
Single Protein Conformation
Multiple Protein Conformations
Limitations of Static Docking
MOLECULAR DYNAMICS (MD) SIMULATIONS
Molecular Dynamics Simulations and Conformational Space Search
Molecular Dynamics Simulations for Enhanced Sampling
Collective Variables Methods (CV)
Metadynamics (mMD)
Umbrella Sampling (US)
Steered Molecular Dynamics (SMD)
Collective Variables-Free Methods
Replica Exchange Molecular Dynamics (REMD)
ENSEMBLE DOCKING
Applications of Ensemble Docking in CADD
HYBRID DOCKING-MD SIMULATIONS APPROACH FOR ENSEMBLE DOCKING
Construction of Ensemble/Conformations by Using MD Simulations
Post-Processing Docked Protein-Ligand Complexes Using MD Simulations
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
References
Molecular Dynamics Applied to Discover Antiviral Agents
Abstract
Introduction
Viral Diseases and Their Threat to Society
Molecular Dynamics Simulations in Drug Design: Fundamentals and Applications
A Brief Theoretical Rationale
Force Fields (FF)
Computational Resources
QM/MM Methods in MD Simulations
MM-PBSA/GBSA Calculations
Applications of Molecular Dynamics on Drug Design
Molecular dynamics methods in machine learning
Drug Discovery of Antivirals
Influenza (INFV)
Neuraminidase (NA) Inhibitors
RNA-dependent RNA Polymerase (RdRp) Inhibitors from INFV
Hemagglutinin (HA) Inhibitors
Zika Virus (ZIKV)
NS2B-NS3 Inhibitors from ZIKV
E Protein Inhibitors from ZIKV
NS5 (RdRp and MTase) Inhibitors from ZIKV
Axl receptor inhibitors used against ZIKV
Dengue Virus (DENV)
NS2B-NS3 Inhibitors from DENV
E Protein Inhibitors from DENV
Human Hexokinase II (HKII) Inhibitors Used \Against DENV
Chikungunya Virus (CHIKV)
nsP2 and nsP3 Inhibitors from CHIKV
Coronaviruses (CoV)
3CLpro Inhibitors from CoV
Spike (S) Glycoprotein Inhibitors from CoV
RNA-dependent RNA Polymerase (RdRp) Inhibitors from CoV
Ebola Virus (EBOV)
Glycoproteins (GPs) Inhibitors from EBOV
VPs Proteins Inhibitors from EBOV
HIV
Protease (PR) Inhibitors from HIV
Reverse Transcriptase (RT) Inhibitors from HIV
Integrase (IN) Inhibitors from HIV
Capsid (CA) Protein Inhibitors from HIV
Challenges, Limitations, and Opportunities
Conclusion and Future Outlooks
List of Abbreviations
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
Acknowledgments
References
Pharmacophore Modeling Approach in Drug Discovery Against the Tropical Infectious Disease Malaria
Abstract
INTRODUCTION
PHARMACOPHORE MODELING
MALARIA
Protease Enzymes
Electron Transport Chain Enzymes
Folate Pathway Enzymes
Fatty Acid Biosynthesis Enzymes
Glycolytic Pathway Enzymes
Isoprenoid Biosynthesis Enzyme
Miscellaneous Targets
Phosphocholine Cytidylyltransferase Enzyme
Apical Membrane Antigen 1 Protein
Orotidine-5-Monophosphate Decarboxylase Enzyme
Stage-V mature Gametocytes
S-Adenosylhomocysteine Hydrolase Enzyme
Peptide Deformylase Enzyme
Purine Nucleoside Phosphorylase
Bromodomain-Contain Protein 1
Superoxide Dismutase Protein
Calcium-Dependent Protein Kinase
Subtilisin-Like Protease 1
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Advances in Computational Network Pharmacology for Traditional Chinese Medicine (TCM) Research
Abstract
INTRODUCTION
COMPUTATIONAL NETWORK PHARMACOLOGY ON TCM
Active Compounds Mining
Compound-Target Interactions Prediction
Network-based Model
Machine Learning-based Method
Bipartite Graph Learning Method
Gene Ontology Enrichment and Pathway Analysis
Network Construction and Topology Analysis
NETWORK PHARMACOLOGY FOR MECHANISM ELUCIDATION OF TCM AGAINST COVID-19
CONCLUDING REMARKS
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
ABBREVIATIONS
REFERENCES
Progress in Electronic-Structure Based Computational Methods: From Small Molecules to Large Molecular Systems of Biological Significance
Abstract
Introduction
Accurate Ab Initio Methods
General Formalism of CI Wave Functions
Practical Implementations of CI-based Approaches to Achieve High-accuracy
CEEIS-FCI Method
Accurate Methods Based on the Renormalization Group Approach
Highly Accurate Ab Initio Methods Based on Quantum Monte Carlo Methodology
Fragment-based approaches for Applications of Biochemical or Pharmaceutical Interest
DFT Advances and Multi-scaling Methodologies QM/MM and QM/MM/MD.
Initiating DFT
DFT Advances
Multi-scaling Methodologies QM/MM and QM/MM/MD
Perboranation of Aza-derivatives of Aromatic Five and Six-membered Rings: A Computational Review
Introduction
Computational Methods
Results and Discussion
Metadynamics
Vertical Singlet-triplet Energy Gaps
Atoms-in-Molecules (AIM) Analysis
Conclusions
Consent for Publication
Conflict of interest
Acknowledgments
references
Frontiers in Computational Chemistry
(Volume 6)
Edited by
Zaheer Ul-Haq
Dr. Panjwani Center for Molecular Medicine & Drug Research,
International Center for Chemical & Biological Sciences,
University of Karachi,
Karachi,
Pakistan
&
Angela K. Wilson
Department of Chemistry,
Michigan State University,
East Lansing, MI,
USA
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PREFACE
Computational chemistry is a very diverse field that uses computer simulation to assist in solving chemical problems. By using methods of theoretical chemistry, incorporated into computer programs, we can calculate the structures and properties of molecules. In general, computational results normally complement the information obtained by chemical experiments, It is widely used in the design of new drugs and materials. The focus of Frontiers in Computational Chemistry is to present different techniques used in drug discovery and the drug development process. Topics falling under this umbrella include computer aided molecular design, drug discovery and development, lead generation, lead optimization, database management, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including analyses of biological activity. In this volume, we have collected six different perspectives in the application of computational methods towards drug design.
Chapter 1: Computer-aided molecular design in computational chemistry
This chapter combines thermodynamics, and numerical optimization to design good or optimal molecular structures with many of them are completely novel. Advances in chemical modeling in the last few decades have greatly benefited CAMD relating chemical structures to properties at several levels of accuracy (molecular mechanics, semi-empirical, ab initio). Though CAMD often uses semi-empirical modeling techniques for their simplicity and efficiency, new approaches incorporating more accurate methods are emerging. In this chapter, the significant advancement, applications of CAMD in the single component product designs, challenges in progression, and the future perspective in designing the chemical compounds by using “computer-aided molecular design” (CAMD) tools is provided.
Chapter 2: Role of Ensemble Conformational Sampling Using Molecular Docking & Dynamics in Drug Discovery
Molecular recognition involved in protein interaction with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. These interactions can be studied through multiple computational approaches including docking, MD simulation etc. In this chapter, the theoretical background of molecular docking, classical MD simulations, MD-based enhanced sampling methods and hybrid docking-MD based methods are highlighted, demonstrating how protein flexibility has been introduced to optimize and enhance accurate protein-ligand binding predictions. Overall, the evolution of various computational strategies is discussed, from molecular docking to molecular dynamics simulations, to improve the overall drug discovery and development process.
Chapter 3: Molecular Dynamics Applied to Discover Antiviral Agents
Molecular Dynamics (MD) remains a valuable tool in optimizing the ligand-protein complexes and understand the ligand binding modes and drug resistance mechanisms in viruses. It is useful for filling in the details about the microscopic events that take place in mere millionths of a second, which experimental methods cannot. Molecular dynamics (MD) simulations utilizes simple approximations based on Newtonian physics to simulate atomic motions. This chapter deals with the concept and applications of MD simulations, as well as their applications in the discovery of drugs against Coronaviruses (SARS-, MERS-CoV, and SARSCoV-2); Influenza (INFV); Chikungunya (CHIKV); Zika (ZIKV); Dengue (DENV); Ebola (EBOV); and human immunodeficiency virus (HIV). This will contribute a great source of helpful information that could be utilized for designing new compounds against neglected diseases.
Chapter 4: Pharmacophore modeling approach in drug discovery against the tropical infectious disease malaria
Despite remarkable improvement in overall global health, Malaria remain a major health problem in the developing world. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of malaria has invigorated the search for new antimalarial drugs. Among computational approaches pharmacophore modelling is widely employed in identifying the new molecules that trigger the desired biological activity. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. This chapter aims to provide the pharmacophore concept, pharmacophore modelling methods and its applications in modern computer-aided drug design.
Chapter 5: Advances in computational network pharmacology for Traditional Chinese Medicine (TCM) research
Traditional Chinese Medicine (TCM) is well-known for its use of medicinal herb combinations to treat the functional disorders which naturally followed the principal of network pharmacology. In this chapter, systematically the methodologies of network pharmacology in TCM studies are discussed followed by its application on TCM against COVID-19. The forefront study examples are also included to collate and analyze the advantages and limitations of different computational techniques.
Chapter 6: Progress in Electronic-Structure Based Computational Methods: From Small Molecules to Large Molecular Systems of Biological Significance
In recent years, understanding of biological systems using electronic structure theory based computational methods with applications to biology and medicine has gained increased interest. Recent computational approaches that account for the effects of electron correlation to a high degree and computational methods that seek to describe large molecular systems with reduced computational cost seek further attention. In this chapter special attention to the computational methods capable of describing phenomena relevant to biological activity and drug discovery and development, as well as the design of new materials relevant to understanding complex biological systems are highlighted.
Zaheer Ul-Haq
Dr. Panjwani Center for Molecular Medicine and Drug Research
International Center for Chemical and Biological Sciences
University of Karachi
Karachi
Pakistan&Angela K. Wilson
Department of Chemistry
Michigan State University
East Lansing, MI
USA
List of Contributors
Anu ManhasDepartment of Chemistry, Pandit Deendayal Energy University (Former PDPU), Gandhinagar-382426, IndiaDemeter TzeliLaboratory of Physical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, Athens 157 84, Athens, Greece
Theoretical and Physical Chemistry Institute, National Hellenic Research Foundation, 48 Vassileos Constantinou Ave, Athens 116 35, GreeceDouglas J. KleinTexas A&M University at Galveston, Galveston, TX 77550, USAEdeildo Ferreira da Silva-JúniorChemistry and Biotechnology Institute, Federal University of Alagoas, Maceió, Brazil
Laboratory of Medicinal Chemistry, Pharmaceutical Sciences Institute, Federal University of Alagoas, Maceió, BrazilHira AnwarDepartment of Chemistry, University of Agriculture, Faisalabad, 38040, PakistanIbon AlkortaInstituto de Química Médica, IQM-CSIC, Juan de la Cierva 3, 28006 Madrid, SpainIgor José dos Santos NascimentoChemistry and Biotechnology Institute, Federal University of Alagoas, Maceió, BrazilJaved IqbalDepartment of Chemistry, University of Agriculture, Faisalabad, 38040, PakistanJosé ElgueroInstituto de Química Médica, IQM-CSIC, Juan de la Cierva 3, 28006 Madrid, SpainJosep M. Oliva-EnrichInstituto de Química-Física “Rocasolano”, IQFR-CSIC, Serrano 119, 28006 Madrid, SpainLaimutis BytautasDepartment of Chemistry, Galveston College, 4015 Av. Q, Galveston, TX 77550, USAMahvish AbbasiDepartment of Chemistry, University of Agriculture, Faisalabad, 38040, PakistanMaxime FerrerInstituto de Química Médica, IQM-CSIC, Juan de la Cierva 3, 28006 Madrid, Spain
Doctoral Programme in Theoretical Chemistry and Computational Modelling, Doctoral School, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, SpainMohd AtharCenter for Chemical Biology and Therapeutics, InStem, Bangalore-560065, Karnataka, IndiaMuhammad Adnan IqbalDepartment of Chemistry, University of Agriculture, Faisalabad, 38040, Pakistan
Organometallic & Coordination Chemistry Laboratory, University of Agriculture, Faisalabad 38040, PakistanMunazzah YaqoobDepartment of Chemistry, University of Agriculture, Faisalabad, 38040, PakistanPatel DhavalDepartment of Biological Sciences and Biotechnology, School of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar-382426, IndiaPrakash JhaSchool of Applied Material Sciences, Central University of Gujarat, Gandhinagar-382030, Gujarat, IndiaShi-Jun YueKey Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, and State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, ChinaSiddhi KediyaSchool of Applied Material Science, Central University of Gujarat, Gandhinagar-382030, IndiaThakor RajkishanDepartment of Biological Sciences and Biotechnology, School of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar-382426, IndiaThiago Mendonça de AquinoChemistry and Biotechnology Institute, Federal University of Alagoas, Maceió, BrazilWen-Xiao WangKey Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, and State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, ChinaYu-Ping TangKey Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, and State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, ChinaYu-Xi HuangKey Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, and State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, China
Computer-Aided Molecular Design in Computational Chemistry
Munazzah Yaqoob1,Mahvish Abbasi1,Hira Anwar1,Javed Iqbal1,Muhammad Adnan Iqbal1,2,*
1 Department of Chemistry, University of Agriculture, Faisalabad, 38040, Pakistan
2 Organometallic & Coordination Chemistry Laboratory, University of Agriculture, Faisalabad 38040, Pakistan
Abstract
In molecular design techniques, thermodynamic properties are predicted through computational tools. Besides, the simple prediction methods explain the space of molecular design while quantum mechanics can accurately predict the properties without any kind of experimental data; however, it is a bit challenging. Therefore, in this chapter, the significant advancement, demurrers in progression, and the future perspective in designing the chemical compounds via using “computer-aided molecular design” (CAMD) tools will be elucidated. Since the interest in designing novel and advanced compounds is increasing with time, traditional methods are not efficient now. This is the key factor in the advancement of CAMD tools. The work advancement different classes of methods that predict the properties will be explained in the chapter. Applications of CAMD in the single component product designs, mixture designs, and also in integrated product designs will be evaluated. All the difficulties while operating the designs and also in obtaining the results and future perspectives will be reviewed. COSMO-CAMD successfully designs novel promising solvents in the liquid-liquid extraction of phenol from water; therefore, it will be explained thoroughly. Some would debate that theoretical tools in computational chemistry can now come up with eager understandings of any chemical process. Yet, the goblet of effective and reliable prediction of compound reactivity has remained fugitive. Favorably, recent developments in the electronic structure theory, which is based on both concepts, element, and rank-scanty, along with the appearance of the highly sophisticated computer architecture, prominently increased the time and length scales that can be simulated using molecular dynamics. This opens the door for the newly proposed ab initio nanoreactor method. Therefore, ab initio methods will be studied completely because we argue that due to this development in molecular designs, the holy grail of computational discovery for complex chemical reactivity is entirely within our reach.
Keywords: CAMD, COSMO, DFT, Geometry Optimization, in silico, IZA.
*Correspondence Muhammad Adnan Iqbal: Department of Chemistry, University of Agriculture, Faisalabad-38040, Pakistan and Organometallic and Coordination Chemistry Laboratory, University of Agriculture Faisalabad-38040, Pakistan ,Tel:03344594372, E-mail:
[email protected]CONSENT FOR PUBLICATION
Not applicable.
Conflict of Interest
The authors declare no conflict of interest.
Acknowlegments
The authors are thankful to the University of Agriculture, Faisalabad, Pakistan, for providing the necessary facilities to accomplish this report. Also, the authors are thankful to the Higher Education Commission of Pakistan for providing research grant NRPU-8198.
References
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