Computer-Aided Drug Design of Plant-Based Compounds

Ion channels are widely expressed in living cells and play critical roles in various cellular biological functions. Dysfunctional ion channels can cause a variety of diseases, making ion channels attractive targets for drug discovery. Computational approaches, such as molecular docking and molecular dynamic simulations, provide economic and efficient tools for finding modulators of ion channels and for elucidating the action mechanisms of small molecules. In this review, we focus primarily on four types of ion channels (voltage-gated, ligand-gated, acid-sensing, and virus matrix 2 ion channels). The current advancements in computer-aided drug discovery and design targeting ion channels are summarized. First, ligand-based studies for drug design are briefly outlined. Then, we focus on the structurebased studies targeting pore domains, endogenous binding sites and allosteric sites of ion channels. Moreover, we also review the contribution of computational methods to the field of ligand binding and unbinding pathways of ion channels. Finally, we propose future developments for the field.

A drug takes many years to develop and reach the market using the conventional drug discovery procedure. Computer-aided drug design (CADD) is an emerging technology that accelerates the process of drug discovery and minimizes the total expenditure associated with labour and resources. In the current scenario, the computational aided drug design (CADD) techniques play a significant role in the design and development of lead molecules for the treatment of various lethal pathological conditions. The prediction of the tertiary structure of a protein is a big concern in drug design and discovery. A typical drug discovery procedure starts with the tertiary structure of a protein. At present, a total of 184,407 protein structures are available in the protein data bank, which are determinedusing experimental methods. However, the procedures are difficult and time-consuming. A more advanced technique has been developed for the prediction of the 3D structure of a protein using a computational method. This technique has played a vital role in drug discovery. It has not only facilitated but also hastened the process of drug discovery. The method is named homology modeling since it involves the building of a protein model based on its homology to similar evolutionary proteins. The method is based on the fact that evolutionary related proteins have similar structures. In homology modelling, the 3D structure of a protein is derived from its primary sequence based on its similarity to the existing protein templates. There are many computational tools for homology modelling such as Modeller, Swiss model, Composer, 3D-JIGSAW, etc. The proposed book chapter will cover the introduction to homology modelling, step-by-step guide to building a protein model, various challenges and how to refine and validate the model, different algorithms related to sequence alignment, similarity search, and the applications of homology modelling in drug design and discovery. The chapter would be very fruitful to the readers to get insights into protein modelling, which will facilitate their research activities. It will be of great application in various disciplines,such as bioinformatics, physics, structural biology, and molecular biology. The content of the chapter will cover various research papers, review papers, and corresponding reference books.

Introduction The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing. Areas covered In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies. Expert opinion There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

Hi reddit! The Zika virus outbreak in the Americas has caused global concern. To help advance the fight against this debilitating virus, we launched OpenZika. OpenZika is a project running on World Community Grid, an IBM philanthropic initiative which provides scientists with free, massive computing resources, donated by volunteers worldwide. Specifically, we’re enlisting the help of World Community Grid volunteers to run docking experiments against crystal structures and homology models of Zika proteins (and other related flavivirus targets that are structurally similar) on their computers and Android devices. We are harnessing World Community Grid’s massive computational power to search through thousands of current drugs (to see if they can be re-purposed against Zika) and millions of drug-like compounds (to lay the foundation for subsequent drug development against Zika). After we have selected and our collaborators have tested compounds that could be effective in killing the Zika virus, we will publish our data and results and share them with the public. As soon as we have proven that some of the candidate compounds can actually either (a) prevent the replication of the Zika virus in cell-based tests or (b) prevent the virus from infecting cells, we and other labs can then modify and evolve these molecules to increase their potency against the virus, improve their other properties (such as solubility, permeability, and metabolic stability), and reduce their toxic side effects, to advance and accelerate the discovery and development of new antiviral drugs against the Zika virus. Carolina Horta Andrade – I am Adjunct Professor at Faculty of Pharmacy of Federal University of Goias, Brazil, and head of LabMol – Laboratory for Molecular Modeling and Drug Design. My research focuses on Computer-Aided Drug Design (CADD) for Neglected Tropical Diseases and Cancer, using an integration of computational and experimental approaches in order to identify new hit and lead compounds for malaria, tuberculosis, leishmaniosis, schistosomiasis, dengue, Chagas disease, as well as for cancer. My group is also focused on the development of in silico tools to predict ADME and toxicity properties of chemical compounds, and development of web platforms as alternatives for animal testing. My laboratory is working in collaboration with many researchers in the US and Europe, as well as in Brazil, integrating computational and experimental approaches to drug design and discovery. We believe that drug discovery is an interdisciplinary process and we need to collaborate to advance science. Alexander L. Perryman - I am a senior researcher in Joel Freundlich’s lab at the Rutgers University, New Jersey Medical School. I have been studying protein structures and how they interact with other molecules for 2 decades. For the past 18 years, I have been developing and applying computational approaches to help advance drug discovery and development research, with a focus on discerning mechanisms of multi-drug resistance and figuring out how to defeat them. I devoted a couple years to cancer research at MU, followed by a dozen years working on HIV at UCSD and TSRI (including running the day-to-day operations of FightAIDS@Home on World Community Grid for 6 years). I also designed and ran the GO Fight Against Malaria (GO FAM) project on World Community Grid, which is when I began working on malaria and tuberculosis. In the Freundlich lab, I am the computational core that helps guide our research on tuberculosis and the drug-resistant ESKAPE pathogens (such as MRSA). Sean Ekins- I am CEO of Collaborations Pharmaceuticals, Inc. I have spent 20 years working on using computers to help drug discovery. Over the last 8 years I have worked on neglected diseases like tuberculosis, Chagas disease, Ebola and Zika. We will be back at 4 pm ET to answer your questions, ask us anything!

Molecular ConceptorTM (http://www.molecular-conceptor.com) is a drug design e-courseware that has been developed as a reference, a source of inspiration for medicinal chemists and a database of case studies in drug discovery. Synergix has developed Molecular Conceptor, as drug design e-courseware that can be used as a reference by medicinal chemists as a source of inspiration in drug design, and as an expanding compendium of knowledge in drug discovery. Molecular Conceptor includes 40 volumes and chapters that cover a wide range of topics such as structure-based drug design, ligand-based drug design, cheminformatics, molecular similarity, 3D-QSAR (three-dimensional Quantitative Structure-Activity Relationships), peptidomimetics, molecular docking and many others. In this report, we have chosen ‘molecular mimicry’ as a representative example to illustrate how it appears in Molecular Conceptor. This topic appears in many sections of the e-courseware, including: (i) three chapters devoted to pharmacophore-based drug design and (ii) several chapters on molecular similarity, 3D database searching, principles of rational drug design and library design. Two-dimensional (2D) representations are often misleading for understanding SAR, and one of the key features of Molecular Conceptor is its ability to visualize molecules in 3D, which is crucial to grasping the subtle recognition and discrimination processes of molecular machinery. In the example illustrated in Figure 1, moving the nitrogen atom in the pyrimidine heterocycle from A to B turns an inactive molecule into a very potent one. This is extremely difficult to see in the 2D formulas. A cogent explanation is given in Molecular Conceptor that can only be derived by viewing the molecules in 3D. Molecules A and B differ solely in the position of the nitrogen atom in the pyrimidine ring and exhibit very different biological activities (left). A possible explanation for this difference is visualized in three-dimension (right). Molecular mimicry is extremely useful in the early stages of drug discovery. When little knowledge is available, such as for example, one active reference compound, this is enough and the project can start. The aim of many such projects was described by terms such as ‘non-peptides, non-nucleotides, non-steroids, non-tricyclic mimetics’ and so forth. Snapshots of some pages associated with this topic are available in Figure 2 and Supplementary Material to this report. Simple and more complex case studies are presented, decomposed and assessed so that methodological rules can be deduced. The core topic is presented in three successive chapters: the first discusses ways to analyse the project (principles of analyses, control of molecular geometries, misuse of structural information, managing hypotheses); the second addresses the design issue (e.g. the four design methods) and the third discusses success stories from 13 different therapeutic areas. Other chapters such as the ones dealing with 3D-QSAR, molecular similarity, library design and ADME (Absorption, Distribution, Metabolism, Elimination) properties highlight the importance of molecular mimicry in these different areas. When molecular mimicry is applied to peptides, it is referred to as ‘peptidomimetics’. This type of approach is mandatory in drug research when dealing with a peptide molecule that cannot be developed as a drug. This topic is presented in detail in two different chapters: (i) Introduction to Peptidomimetics and (ii) Peptidomimetic Examples. However, many other examples are presented for diverse purposes in other sections of the software. For instance, the successful design of Aliskiren (a renin inhibitor now in late stages of development) is presented in both the chapter on examples of peptidomimicry as well as in the chapter on case histories in drug discovery. Snapshot of a page from Molecular Conceptor. In conclusion, the concept of molecular similarity has nurtured the imagination and creativity of several generations of medicinal chemists. Because of the fuzziness of the concept, medicinal chemists live with the contradiction that similar compounds simultaneously have and do not have similar activities. The key concepts and features of thousands of scientific articles devoted to this topic are summarized in Molecular Conceptor. The essence of millions of hours of scientific work, human creativity, investment in human resources and expenditures in R & D are presented on a friendly and efficient platform. Molecular Conceptor is rapidly becoming a prime reference for medicinal chemists, a source of inspiration in drug design and can also be used to train newly recruited medicinal chemists in the industry (1-4). Figure S1: Snapshots of pages from Molecular Conceptor dealing with molecular mimicry. For clarity, both the navigation tree and the text of the pages have been omitted Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Some species of Cordyceps sensu lato are famous Chinese herbs with significant biological activities, often used as edible food and traditional medicine in China. Cordyceps represents the largest entomopathogenic group of fungi, including 40 genera and 1339 species in three families and incertae sedis of Hypocreales. Most of the Cordyceps-derivatives have been approved clinically for the treatment of various diseases such as diabetes, cancers, inflammation, cardiovascular, renal and neurological disorders and are used worldwide as supplements and herbal drugs, but there is still need for highly efficient Cordyceps-derived drugs for fatal diseases with approval of the U.S. Food and Drug Administration. Computer-aided drug design concepts could improve the discovery of putative Cordyceps- derived medicine within less time and low budget. The integration of computer-aided drug design methods with experimental validation has contributed to the successful discovery of novel drugs. This review focused on modern taxonomy, active metabolites, and modern drug design techniques that could accelerate conventional drug design and discovery of Cordyceps s. l. Successful application of computer-aided drug design methods in Cordyceps research has been discussed. It has been concluded that computer-aided drug design techniques could influence the multiple target-focused drug design, because each metabolite of Cordyceps has shown significant activities for the various diseases with very few or no side effects.

ObjectivePresent clinicians with an updated overview of empagliflozin for the treatment of type 2 diabetes mellitus (T2DM), with focus on its use in combination regimens. MethodsUsing the Medline database, keyword searches were undertaken to identify literature reporting the use of empagliflozin treatment in clinical trials with a minimum duration of 12 weeks relating to patients with T2DM.

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.

With the advances in medicine, people have deeply understood the complex pathogenesis of diseases. Revealing the mechanism of action and therapeutic effect of drugs from an overall perspective has become the top priority of drug design. However, the traditional drug design methods cannot meet the current needs. In recent years, with the rapid development of systems biology, a variety of new technologies including metabolomics, genomics, and proteomics have been used in drug research and development. As a bridge between traditional pharmaceutical theory and modern science, computer-aided drug design(CADD) can shorten the drug development cycle and improve the success rate of drug design. The application of systems biology and CADD provides a methodological basis and direction for revealing the mechanism and action of drugs from an overall perspective. This paper introduces the research and application of systems biology in CADD from different perspectives and proposes the development direction, providing reference for promoting the application.

Drug design through computer, a recent, very effective technique in modern arena. Now a days Computer Aided Drug Design (CADD) technologies are used in nanotechnology, molecular biology, biochemistry etc. The main benefit of the CADD is cost effective in research and development of drugs. There are wide ranges of software are used in CADD, Grid computing, window based general PBPK/PD modeling software, PKUDDS for structure based drug design,APIS, JAVA, Perl and Python, CADD as well as software including software libraries. There are different techniques used in CADD visualization, homology, molecular dynamic, energy minimization molecular docking, QSAR etc. Computer aided drug design is applicable in Cancer disease, transportation of drug to specific site in body, data collections and storages of organics and biologicals. Conformational properties and energetics of small molecules and DNA cleavage, molecular diagnostics based on fluorescences are focusing using this technique. DOI: http://dx.doi.org/10.3329/ijpls.v1i2.12955 International Journal of Pharmaceutical and Life Sciences Vol.1(2) 2012

Drug discovery in pharmaceutical research is a very time taking process. It takes around about 10 to 14 years reached to clinical trial. Chances of discovery of novel molecule reached to clinical trial are very less. Also, it takes billion of investment. Biomedical engineering with new technologies is the answer to these problems. The purpose of this review is to focus on the applications of new technologies like computer-aided drug design, virtual screening, molecular docking, 3D printing, etc. in the field of pharmaceutical research, drug delivery system, and drug discovery. So it is very important for all the researchers and pharmacy departments to be aware of the benefits or applications of these modern technologies. These modern technologies are very useful for pharmaceutical research including pharmacokinetics or pharmacodynamics modeling, optimization of the drug dosage form, and drug delivery system. The current progress of docking-based virtual screening leads to finding out the new target molecule and designing the molecule by using computer-aided drug design and ultimately manufacturing or customization of the dosage form by using 3D printing may finally lead to the become a reality.

Chapter 11 - Fundamentals of drug design and discovery

Coronavirus (CoV) diseases are widespread throughout the world and have caused considerable socio-economic disruptions. For this reason, efforts have been made to develop a direct or indirect antiviral drugs against these diseases. However, no specific antiviral drug has yet been approved by the Food and Drug Administration (FDA) for CoV infections. Thus, the challenge in discovering therapeutic molecules against these infections remains pertinent. Computer-aided drug design (CADD) is one of the modern techniques for drug discovery and development. It accelerates the process, minimizes costs, and reduces research time. In this article, we present the three CADD approaches, namely structure-based drug discovery (SBDD), ligand-based drug discovery (LBDD) and high-throughput virtual screening (HTVS). The different methods used in these three approaches CADD, such as molecular modelling, target structure analysis, molecular docking, molecular dynamics simulation, pharmacophore modelling, quantitative structure-activity relationship (QSAR), ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) are detailed. In addition, the bioinformatics tools and databases commonly used in these different CADD techniques are also described.