Query: Computer aided drug design

title: Computer Aided Drug Design for the Identification of Novel Antischistosomal Compounds.
abstract: Schistosomiasis is a chronic neglected tropical disease, highlighted by the presence of Schistosoma worms, which presents in advanced cases in approximately 80 countries, affecting almost 300 million people. The treatment is based on only one drug, praziquantel, a drug discovered in the 1970s that shows moderate efficacy against the adult parasite, but low efficacy against the larval stages of the parasite. Therefore, the use of only one drug has brought concerns and losses on drug resistance cases, necessitating the development of new effective chemotherapeutic agents against Schistosoma species. One of the strategies that have been implemented in drug development is the computer aided drug design (CADD) investigating the structural characteristics of the compounds and targets in order to understand their actions and biological activities through 3D virtual manipulation, as the QSAR applied to ligands and molecular docking applied to a respective biological target. These studies help to extract information and characteristics relevant to the activity, as well as to predict potential applications and activity. Therefore, this chapter will present the main validated biological targets of the genus Schistosoma, as thioredoxin glutathione reductase (TGR) histone deacetylases (HDAC 1, HDAC 8) dihydroorotate dehydrogenase, sirtuin protein and cathepsin L1, as well as reports of CADD in literature applied to the development of drugs against schistosomiasis, providing compounds with high pharmacological potential and high specificity.

title: Computer Aided Drug Design Applied to Secondary Metabolites as Anticancer Agents.
abstract: Aided Drug Design (CADD) techniques have garnered a great deal of attention in academia and industry because of their great versatility, low costs, possibilities of cost reduction in in vitro screening and in the development of synthetic steps; these techniques are compared with high throughput screening, in particular for candidate drugs. The secondary metabolism of plants and other organisms provide substantial amounts of new chemical structures, many of which have numerous biological and pharmacological properties for virtually every existing disease, including cancer. In oncology, compounds such as vimblastine, vincristine, taxol, podophyllotoxin, captothecin and cytarabine are examples of how important natural products enhance the cancer fighting therapeutic arsenal.  In this context, this review presents an update of Ligand Based Drug Design and Structure Based Drug Design techniques applied to flavonoids, alkaloids and coumarins in the search of new compounds or fragments that can be used in oncology.  A systematical search using various databases was performed. The search was limited to articles published in the last 10 years.  The great diversity of chemical structures (coumarin, flavonoids and alkaloids) with cancer properties, associated with infinite synthetic possibilities for obtaining analogous compounds, creates a huge chemical environment with potential to be explored, and creates a major difficulty, for screening studies to select compounds with more promising activity for a selected target. CADD techniques appear to be the least expensive and most efficient alternatives to perform virtual screening studies, aiming to selected compounds with better activity profiles and better "drugability"

title: Benchmarking of Semiempirical Quantum Mechanical Methods on Systems Relevant to Computer Aided Drug Design
abstract: The semiempirical quantum mechanical (SQM) methods used in drug design are commonly parametrized and tested on data sets of systems that may not be representative models for drug biomolecule interactions in terms of both size and chemical composition. This is addressed here with a new benchmark data set, PLF547, derived from protein ligand complexes, consisting of complexes of ligands with protein fragments (such as amino acid side chains) with interaction energies based on MP2 F12 and DLPNO CCSD(T) calculations. From these, composite benchmark interaction energies are also built for complexes of the ligand with the complete active site of the protein (PLA15 data set) These data sets are used to test multiple SQM methods with corrections for noncovalent interactions; the role of the solvation model in the calculations is tested as well.

title: Computer Aided Drug Design for the Organic Chemistry Laboratory Using Accessible Molecular Modeling Tools
abstract: Drug design and development is a central thrust of current research. Thus, it is imperative that students pursuing degrees in a variety of chemical and biological fields become exposed to the resea.

title: A REVIEW ON COMPUTER AIDED DRUG DESIGN (CAAD) AND IT'S IMPLICATIONS IN DRUG DISCOVERY AND DEVELOPMENT PROCESS
abstract: Discovery and development of new drug is generally known as a very complex process which is a time consuming process. So, computer aidded drug design approaches are used very widely to increase the efficency of the drug. Many approach of CADD are evaluated a promising techniques in all the structure based drug design and ligand based are known efficient and powerful techniques in drug development and discovery. So, both methods can be applied with molecular docking to virtual screen for identification and optimisation. Computational tools are widely used in pharmaceutical industries and research with many facets. The theoretical basis of CADD uses quantum mechanics and molecular modelling studies. It's also based on the database searching and binding affinity on the basis of biological target. Computational techniques reduces the cost upto 50% in the drug design process. In review we give an view of computational approaches, CADD tools and current advancement in the field of chemistry.

title: Computer aided drug design against spike glycoprotein of SARS CoV 2 to aid COVID 19 treatment
abstract: SARS CoV 2 has the Spike glycoprotein (S) which is crucial in attachment with host receptor and cell entry leading to COVID 19 infection. The current study was conducted to explore drugs against Receptor Binding Domain (RBD) of SARS CoV 2 using in silico pharmacophore modelling and virtual screening approach to combat COVID 19.  All the available sequences of RBD in NCBI were retrieved and multiple aligned to get insight into its diversity. The 3D structure of RBD was modelled and the conserved region was used as a template to design pharmacophore using LigandScout. Lead compounds were screened using Cambridge, Drugbank, ZINC and TIMBLE databases and these identified lead compounds were screened for their toxicity and Lipinski's rule of five. Molecular docking of shortlisted lead compounds was performed using AutoDock Vina and interacting residues were visualized.  Active residues of Receptor Binding Motif (RBM) in S, involved in interaction with receptor, were found to be conserved in all 483 sequences. Using this RBM motif as a pharmacophore a total of 1327 lead compounds were predicted initially from all databases, however, only eight molecules fit the criteria for safe oral drugs.  The RBM region of S interacts with Angiotensin Converting Enzyme 2 (ACE2) receptor and Glucose Regulated Protein 78 (GRP78) to mediate viral entry. Based on in silico analysis, the lead compounds scrutinized herewith interact with S, hence, can prevent its internalization in cell using ACE2 and GRP78 receptor. The compounds predicted in this study are based on rigorous computational analysis and the evaluation of predicted lead compounds can be promising in experimental studies.

title: Members of our early career panel highlight key research articles on the theme of computer aided drug design

title: Screening of drug efficacy of Rosmarinic Acid derivatives as Aurora kinase inhibitors by computer aided drug design method.
abstract: Aurora kinases (AKs) belong to serine/threonine kinase family, play a crucial role in regulating the cell cycle. Therefore, AKs are the hopeful target for anticancer therapies and these finding have appreciated researchers to rigorous hunting of small molecule aurora kinase inhibitors, not only for research articles but also use as therapeutic agent.  The present study helps us to identify and screen best phytochemicals as potent inhibitors against AKs. These potent inhibitors are coming from various substitution of rosmarinic acid (RA) METHODS In this paper, we choose different tested derivative compounds for designing anticancer drugs by substituting various functional groups of standard drug RA. In silico studies were carried out, in an effort to appreciate better drug candidature of some of these derivative compounds. This study wasperformed on 56 derived compounds of standard RA. DFT study was done using UB3LYP/6 311G++G(d,p) basis set to study HOMO LUMO energies, dipole moments, using Gaussian16 suite. Some of the derived parameters, like ionization potential, electron affinity, softness hardness, chemical potential and electrophilicity index were noted. Docking study was performed with AKs inhibiting receptor using AutoDock 4.2. ADME prediction was done with preADMET web tool. Molecular descriptor properties were predicted with molinspiration and OSIRIS property explorer.  Out of 56 derivatives, 11 have passed all the rules of drug candidature, to serve as best AKs inhibitor, in a theoretical manner.  This study should be supported a new proposal to explore future studies with these 11 compounds against cancer.

title: Computer Aided Drug Design
abstract: Drug discovery is an expensive and complicated process. The drug must fulfill some criteria of being nontoxic, bioavailable, and potent. In the view of evermore stringent demands about efficacy, potency, and safety, the finding of the new drug like molecule has become a complex and resource intensive undertaking. Now, the availability of 3D structures of molecular drug targets and advances in computational approaches and bioinformatics speed up the application of molecular modeling in discovery. In this chapter, several molecular modeling strategies employed in modern drug discovery program are discussed. The concepts of structureand ligand based drug designing, protein modeling and visualization, molecular docking, virtual screening, molecular dynamics simulation, pharmacophore modeling, and QSAR approaches have been explained. Besides, we also provide important database resources and tools available for drug research. Finally, we present case studies conducted in our lab, showing how computational approaches can be implemented in reality for the discovery and designing of novel drugs from natural sources. R. K. Pathak M. Baunthiyal Department of Biotechnology, Govind Ballabh Pant Institute of Engineering Technology, Pauri Garhwal, Uttarakhand, India D. B. Singh Department of Biotechnology, Institute of Biosciences and Biotechnology, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh, India M. Sagar Department of Bioinformatics, University Institute of Engineering and Technology, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh, India A. Kumar Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India The Editor(s) (if applicable) and The Author(s) under exclusive licence to Springer Nature Singapore Pte Ltd. 2020 D. B. Singh (ed. Computer Aided Drug Design, https:/doi.org/10.1007/978 981 15 6815 2_1 1

title: QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP PHARMACOPHORE MAPPING STUDIES OF FEW NATURAL COMPOUNDS FROM HERBAL PLANTS AGAINST CORONAVIRUS: A COMPUTER AIDED DRUG DESIGN APPROACH
abstract: There was a major outbreak of COVID 19 disease during December 2019 in China and later in March 2020, it was announced by World Health Organization to be a pandemic disease throughout the globe due to its infection all over the world caused by the pathogenic strain of Coronavirus. Currently, there is no known effective treatment and targeted therapeutics to cure COVID 19 disease. Hence there is an emergency need to develop a new drug against this COVID 19 disease. The main aim of the current in silico study is to identify an effective drug that can inhibit the coronavirus without any side effects. Here, we have identified two main target proteins, 6LU7 and 2AJF which are the leading cause of the disease and analyzed their structure using the PDB database. Many studies have already proven that herbal plant compounds, in general, are recognized as healthier products than synthetic drugs. Hence we have selected 247 antiviral phytochemical compounds from 122 herbal plants through a literature survey and analyzed the 2D structure of compounds using the PubChem database. Further, Virtual screening and QSAR (Quantitative Structure Activity Relationship) studies were carried out and we reported the best 4 phytochemical compounds. For the better prediction of interaction between proteins and ligands, we have performed pharmacophore mapping studies and protein dynamics studies using various bioinformatics tools. Finally, Molecular Docking was performed between the reported best compounds and the target proteins to study their interaction profile. The results exhibited the best binding energy and interaction with the active site of the two target proteins. Further in vitro studies with the above compounds would lead to the identification and discovery of novel Coronavirus inhibitor.