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Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Kaur, Navneet, Mymoona Akhter, and Chhavi Singla. "Drug designing: Lifeline for the drug discovery and development process." Research Journal of Chemistry and Environment 26, no. 8 (July 25, 2022): 173–79. http://dx.doi.org/10.25303/2608rjce1730179.

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Анотація:

Drug discovery and development field has entered into a revolutionary phase with the introduction of Computer Aided Drug Designing (CADD) tools in the designing and development of new drugs. Traditional drug discovery and designing is a tedious, expensive and time-consuming process. Pharmaceutical industries spend billions of dollars to launch a potential drug candidate into the drug market. It takes 15-20 years of research to discover a new drug candidate. The advancements in the Computer Aided Drug Designing techniques have significantly contributed towards lowering the cost and time involved in new drug discovery. Different types of approaches are used to find out the potential drug candidates. Numerous compounds have been successfully discovered and launched into the market using computational tools. Various novel software-based methods like Structure- Based Drug Designing (SBDD), Ligand-Based Drug Designing (LBDD), Pharmacophore Mapping and Fragment-Based Drug Designing (FBDD) are considered as powerful tools for determining the pharmacokinetics, pharmacodynamics and structure activity relationship between target protein and its ligand. These tools provide valuable information about experimental findings and the mechanism of action of drug molecules. This has greatly expedited the discovery of promising drug candidates by sidestepping the lengthy steps involved in the synthesis of unnecessary compounds.

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2

MMCCOY, MICHAEL. "DRUG DISCOVERY TOOLS DEBUT." Chemical & Engineering News Archive 80, no. 32 (August 12, 2002): 8. http://dx.doi.org/10.1021/cen-v080n032.p008.

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3

Zhang, Ru, and Xin Xie. "Tools for GPCR drug discovery." Acta Pharmacologica Sinica 33, no. 3 (January 23, 2012): 372–84. http://dx.doi.org/10.1038/aps.2011.173.

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4

Pedreira, Júlia G. B., Lucas S. Franco, and Eliezer J. Barreiro. "Chemical Intuition in Drug Design and Discovery." Current Topics in Medicinal Chemistry 19, no. 19 (October 21, 2019): 1679–93. http://dx.doi.org/10.2174/1568026619666190620144142.

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The medicinal chemist plays the most important role in drug design, discovery and development. The primary goal is to discover leads and optimize them to develop clinically useful drug candidates. This process requires the medicinal chemist to deal with large sets of data containing chemical descriptors, pharmacological data, pharmacokinetics parameters, and in silico predictions. The modern medicinal chemist has a large number of tools and technologies to aid him in creating strategies and supporting decision-making. Alongside with these tools, human cognition, experience and creativity are fundamental to drug research and are important for the chemical intuition of medicinal chemists. Therefore, fine-tuning of data processing and in-house experience are essential to reach clinical trials. In this article, we will provide an expert opinion on how chemical intuition contributes to the discovery of drugs, discuss where it is involved in the modern drug discovery process, and demonstrate how multidisciplinary teams can create the optimal environment for drug design, discovery, and development.

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5

Cheung, Eugene, Yan Xia, Marc A. Caporini, and Jamie L. Gilmore. "Tools shaping drug discovery and development." Biophysics Reviews 3, no. 3 (September 2022): 031301. http://dx.doi.org/10.1063/5.0087583.

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Spectroscopic, scattering, and imaging methods play an important role in advancing the study of pharmaceutical and biopharmaceutical therapies. The tools more familiar to scientists within industry and beyond, such as nuclear magnetic resonance and fluorescence spectroscopy, serve two functions: as simple high-throughput techniques for identification and purity analysis, and as potential tools for measuring dynamics and structures of complex biological systems, from proteins and nucleic acids to membranes and nanoparticle delivery systems. With the expansion of commercial small-angle x-ray scattering instruments into the laboratory setting and the accessibility of industrial researchers to small-angle neutron scattering facilities, scattering methods are now used more frequently in the industrial research setting, and probe-less time-resolved small-angle scattering experiments are now able to be conducted to truly probe the mechanism of reactions and the location of individual components in complex model or biological systems. The availability of atomic force microscopes in the past several decades enables measurements that are, in some ways, complementary to the spectroscopic techniques, and wholly orthogonal in others, such as those related to nanomechanics. As therapies have advanced from small molecules to protein biologics and now messenger RNA vaccines, the depth of biophysical knowledge must continue to serve in drug discovery and development to ensure quality of the drug, and the characterization toolbox must be opened up to adapt traditional spectroscopic methods and adopt new techniques for unraveling the complexities of the new modalities. The overview of the biophysical methods in this review is meant to showcase the uses of multiple techniques for different modalities and present recent applications for tackling particularly challenging situations in drug development that can be solved with the aid of fluorescence spectroscopy, nuclear magnetic resonance spectroscopy, atomic force microscopy, and small-angle scattering.

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6

MacRae, Calum A., and Randall T. Peterson. "Zebrafish as tools for drug discovery." Nature Reviews Drug Discovery 14, no. 10 (September 11, 2015): 721–31. http://dx.doi.org/10.1038/nrd4627.

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7

Weerasekara, Sahani, Allan M. Prior, and Duy H. Hua. "Current tools for norovirus drug discovery." Expert Opinion on Drug Discovery 11, no. 6 (May 2, 2016): 529–41. http://dx.doi.org/10.1080/17460441.2016.1178231.

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8

Ivanenkov, Yan A., Nikolay P. Savchuk, Sean Ekins, and Konstantin V. Balakin. "Computational mapping tools for drug discovery." Drug Discovery Today 14, no. 15-16 (August 2009): 767–75. http://dx.doi.org/10.1016/j.drudis.2009.05.016.

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9

Goff, Aaron, Daire Cantillon, Leticia Muraro Wildner, and Simon J. Waddell. "Multi-Omics Technologies Applied to Tuberculosis Drug Discovery." Applied Sciences 10, no. 13 (July 3, 2020): 4629. http://dx.doi.org/10.3390/app10134629.

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Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery.

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10

Bruno, Agostino, Gabriele Costantino, Luca Sartori, and Marco Radi. "The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization." Current Medicinal Chemistry 26, no. 21 (September 19, 2019): 3838–73. http://dx.doi.org/10.2174/0929867324666171107101035.

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Background: Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. Methods: In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Results: A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. Conclusion: The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

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11

Leelananda, Sumudu P., and Steffen Lindert. "Computational methods in drug discovery." Beilstein Journal of Organic Chemistry 12 (December 12, 2016): 2694–718. http://dx.doi.org/10.3762/bjoc.12.267.

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The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

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12

Gehrtz, Paul, and Nir London. "Electrophilic Natural Products as Drug Discovery Tools." Trends in Pharmacological Sciences 42, no. 6 (June 2021): 434–47. http://dx.doi.org/10.1016/j.tips.2021.03.008.

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13

Caldwell, Garry W., and Barry A. Springer. "Editorial: Cutting Edge Tools in Drug Discovery”." Frontiers in Drug Design & Discovery 1, no. 1 (January 1, 2005): 1–2. http://dx.doi.org/10.2174/1574088054583309.

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14

Nantasenamat, Chanin, and Virapong Prachayasittikul. "Maximizing computational tools for successful drug discovery." Expert Opinion on Drug Discovery 10, no. 4 (February 18, 2015): 321–29. http://dx.doi.org/10.1517/17460441.2015.1016497.

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15

Nestler, Eric J. "Inducible Genetic Tools for CNS Drug Discovery." CNS Drug Reviews 5 (June 7, 2006): 17. http://dx.doi.org/10.1111/j.1527-3458.1999.tb00130.x.

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16

Hagan, Daniel, and Martin Hagan. "Soft Computing Tools for Virtual Drug Discovery." Journal of Artificial Intelligence and Soft Computing Research 8, no. 3 (July 1, 2018): 173–89. http://dx.doi.org/10.1515/jaiscr-2018-0012.

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AbstractIn this paper, we describe how several soft computing tools can be used to assist in high throughput screening of potential drug candidates. Individual small molecules (ligands) are assessed for their potential to bind to specific proteins (receptors). Committees of multilayer networks are used to classify protein-ligand complexes as good binders or bad binders, based on selected chemical descriptors. The novel aspects of this paper include the use of statistical analyses on the weights of single layer networks to select the appropriate descriptors, the use of Monte Carlo cross-validation to provide confidence measures of network performance (and also to identify problems in the data), the addition of new chemical descriptors to improve network accuracy, and the use of Self Organizing Maps to analyze the performance of the trained network and identify anomalies. We demonstrate the procedures on a large practical data set, and use them to discover a promising characteristic of the data. We also perform virtual screenings with the trained networks on a number of benchmark sets and analyze the results.

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17

Eglen, Richard M., and Terry Reisine. "Photoproteins: Important New Tools in Drug Discovery." ASSAY and Drug Development Technologies 6, no. 5 (October 2008): 659–72. http://dx.doi.org/10.1089/adt.2008.160.

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18

Kurtboke, Ipek. "Bacteriophages as tools in drug discovery programs." Microbiology Australia 31, no. 2 (2010): 67. http://dx.doi.org/10.1071/ma10067.

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Screening of microbial natural products continues to represent an important route to the discovery of novel bioactive compounds for the development of new therapeutic or other important industrial agents. However, a continuous supply of diverse compounds is needed to meet the needs of industry. Such a supply can only be derived through systematic screening of bioactive compound-producing microorganisms from natural sources.

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19

Stahl, Andrea M., Michael Adler, Charles B. Millard, and Lynne Gilfillan. "Product development tools to enhance drug discovery." Botulinum J. 1, no. 1 (2008): 7. http://dx.doi.org/10.1504/tbj.2008.018950.

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20

Jawhari, Anass. "Editorial – Membrane protein tools for drug discovery." Methods 180 (August 2020): 1–2. http://dx.doi.org/10.1016/j.ymeth.2020.07.009.

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21

Stocks, Martin. "Intrabodies as drug discovery tools and therapeutics." Current Opinion in Chemical Biology 9, no. 4 (August 2005): 359–65. http://dx.doi.org/10.1016/j.cbpa.2005.06.003.

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22

Zhang, Guojian, Jing Li, Tianjiao Zhu, Qianqun Gu, and Dehai Li. "Advanced tools in marine natural drug discovery." Current Opinion in Biotechnology 42 (December 2016): 13–23. http://dx.doi.org/10.1016/j.copbio.2016.02.021.

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23

Sheik Amamuddy, Olivier, Wayde Veldman, Colleen Manyumwa, Afrah Khairallah, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker, and Özlem Tastan Bishop. "Integrated Computational Approaches and Tools for Allosteric Drug Discovery." International Journal of Molecular Sciences 21, no. 3 (January 28, 2020): 847. http://dx.doi.org/10.3390/ijms21030847.

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Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.

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24

Zagotto, Giuseppe, and Marco Bortoli. "Drug Design: Where We Are and Future Prospects." Molecules 26, no. 22 (November 22, 2021): 7061. http://dx.doi.org/10.3390/molecules26227061.

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Medicinal chemistry is facing new challenges in approaching precision medicine. Several powerful new tools or improvements of already used tools are now available to medicinal chemists to help in the process of drug discovery, from a hit molecule to a clinically used drug. Among the new tools, the possibility of considering folding intermediates or the catalytic process of a protein as a target for discovering new hits has emerged. In addition, machine learning is a new valuable approach helping medicinal chemists to discover new hits. Other abilities, ranging from the better understanding of the time evolution of biochemical processes to the comprehension of the biological meaning of the data originated from genetic analyses, are on their way to progress further in the drug discovery field toward improved patient care. In this sense, the new approaches to the delivery of drugs targeted to the central nervous system, together with the advancements in understanding the metabolic pathways for a growing number of drugs and relating them to the genetic characteristics of patients, constitute important progress in the field.

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25

KIRBOĞA, Kevser Kübra, and Ecir KÜÇÜKSİLLE. "Bilgisayar Destekli İlaç Keşfi Üzerine Bakışlar." Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi 11, no. 2 (December 30, 2022): 1. http://dx.doi.org/10.55007/dufed.1103457.

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The drug development and discovery process are challenging, take 15 to 20 years, and require approximately 1.5-2 billion dollars, from the critical selection of the target molecule to post-clinical market application. Several computational drug design methods identify and optimize target biologically lead compounds. Given the complexity and cost of the drug discovery process in recent years, computer-assisted drug discovery (CADD) has spread over a broad spectrum. CADD methods support the discovery of target molecules, optimization of small target molecules, analysis, and development processes faster and less costly. These methods can be classified into structure-based (SBDD) and ligand-based (LBDD). SBDD begins the development process by focusing on the knowledge of the three-dimensional structure of the biological target. Finally, this review article provides an overview of the details, purposes, uses in developing drugs, general workflows, tools used, limitations, and future of CADD methods, including the SBDD and LBDD processes that have become an integral part of pharmaceutical companies and academic research.

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26

Lenci, Elena, and Andrea Trabocchi. "Peptidomimetic toolbox for drug discovery." Chemical Society Reviews 49, no. 11 (2020): 3262–77. http://dx.doi.org/10.1039/d0cs00102c.

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27

Brogi. "Computational Approaches for Drug Discovery." Molecules 24, no. 17 (August 22, 2019): 3061. http://dx.doi.org/10.3390/molecules24173061.

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28

Chatelain, Eric. "Chagas Disease Drug Discovery." Journal of Biomolecular Screening 20, no. 1 (September 22, 2014): 22–35. http://dx.doi.org/10.1177/1087057114550585.

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American trypanosomiasis, or Chagas disease, is the result of infection by the Trypanosoma cruzi parasite. Endemic in Latin America where it is the major cause of death from cardiomyopathy, the impact of the disease is reaching global proportions through migrating populations. New drugs that are safe, efficacious, low cost, and adapted to the field are critically needed. Over the past five years, there has been increased interest in the disease and a surge in activities within various organizations. However, recent clinical trials with azoles, specifically posaconazole and the ravuconazole prodrug E1224, were disappointing, with treatment failure in Chagas patients reaching 70% to 90%, as opposed to 6% to 30% failure for benznidazole-treated patients. The lack of translation from in vitro and in vivo models to the clinic observed for the azoles raises several questions. There is a scientific requirement to review and challenge whether we are indeed using the right tools and decision-making processes to progress compounds forward for the treatment of this disease. New developments in the Chagas field, including new technologies and tools now available, will be discussed, and a redesign of the current screening strategy during the discovery process is proposed.

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29

Patel, Hershna, and Andreas Kukol. "Recent discoveries of influenza A drug target sites to combat virus replication." Biochemical Society Transactions 44, no. 3 (June 9, 2016): 932–36. http://dx.doi.org/10.1042/bst20160002.

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Sequence variations in the binding sites of influenza A proteins are known to limit the effectiveness of current antiviral drugs. Clinically, this leads to increased rates of virus transmission and pathogenicity. Potential influenza A inhibitors are continually being discovered as a result of high-throughput cell based screening studies, whereas the application of computational tools to aid drug discovery has further increased the number of predicted inhibitors reported. This review brings together the aspects that relate to the identification of influenza A drug target sites and the findings from recent antiviral drug discovery strategies.

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30

Bhattacharya, Arijit, Audrey Corbeil, Rubens L. do Monte-Neto, and Christopher Fernandez-Prada. "Of Drugs and Trypanosomatids: New Tools and Knowledge to Reduce Bottlenecks in Drug Discovery." Genes 11, no. 7 (June 29, 2020): 722. http://dx.doi.org/10.3390/genes11070722.

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Leishmaniasis (Leishmania species), sleeping sickness (Trypanosoma brucei), and Chagas disease (Trypanosoma cruzi) are devastating and globally spread diseases caused by trypanosomatid parasites. At present, drugs for treating trypanosomatid diseases are far from ideal due to host toxicity, elevated cost, limited access, and increasing rates of drug resistance. Technological advances in parasitology, chemistry, and genomics have unlocked new possibilities for novel drug concepts and compound screening technologies that were previously inaccessible. In this perspective, we discuss current models used in drug-discovery cascades targeting trypanosomatids (from in vitro to in vivo approaches), their use and limitations in a biological context, as well as different examples of recently discovered lead compounds.

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31

Shaker, Bilal, Sajjad Ahmad, Jingyu Lee, Chanjin Jung, and Dokyun Na. "In silico methods and tools for drug discovery." Computers in Biology and Medicine 137 (October 2021): 104851. http://dx.doi.org/10.1016/j.compbiomed.2021.104851.

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32

Machina, Hari K., and David J. Wild. "Laboratory Informatics Tools Integration Strategies for Drug Discovery." Journal of Laboratory Automation 18, no. 2 (April 2013): 126–36. http://dx.doi.org/10.1177/2211068212454852.

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33

Huan, Tianxiao, Xiaogang Wu, and Jake Y. Chen. "Systems biology visualization tools for drug target discovery." Expert Opinion on Drug Discovery 5, no. 5 (April 19, 2010): 425–39. http://dx.doi.org/10.1517/17460441003725102.

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34

Crawford, M. "Peptide aptamers: Tools for biology and drug discovery." Briefings in Functional Genomics and Proteomics 2, no. 1 (January 1, 2003): 72–79. http://dx.doi.org/10.1093/bfgp/2.1.72.

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35

Giuliano, Kenneth A., and D. Lansing Taylor. "Fluorescent-protein biosensors: New tools for drug discovery." Trends in Biotechnology 16, no. 3 (March 1998): 135–40. http://dx.doi.org/10.1016/s0167-7799(97)01166-9.

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36

Kwong, Elizabeth, John Higgins, and Allen C. Templeton. "Strategies for bringing drug delivery tools into discovery." International Journal of Pharmaceutics 412, no. 1-2 (June 2011): 1–7. http://dx.doi.org/10.1016/j.ijpharm.2011.03.024.

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37

Modi, Sandeep. "Positioning ADMET in silico tools in drug discovery." Drug Discovery Today 9, no. 1 (January 2004): 14–15. http://dx.doi.org/10.1016/s1359-6446(04)02956-3.

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38

Cooper, Michael E. "Chemoinformatics: Concepts, Methods and Tools for Drug Discovery." Drug Discovery Today 9, no. 22 (November 2004): 957–59. http://dx.doi.org/10.1016/s1359-6446(04)03262-3.

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39

Hannemann, Holger. "Viral replicons as valuable tools for drug discovery." Drug Discovery Today 25, no. 6 (June 2020): 1026–33. http://dx.doi.org/10.1016/j.drudis.2020.03.010.

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40

Rocha-Pereira, Joana, Johan Neyts, and Dirk Jochmans. "Norovirus: Targets and tools in antiviral drug discovery." Biochemical Pharmacology 91, no. 1 (September 2014): 1–11. http://dx.doi.org/10.1016/j.bcp.2014.05.021.

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41

Neefjes, Jacques, and Nico P. Dantuma. "Fluorescent probes for proteolysis: Tools for drug discovery." Nature Reviews Drug Discovery 3, no. 1 (January 2004): 58–69. http://dx.doi.org/10.1038/nrd1282.

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42

Mack, George S. "Pharmas partner in venture seeking drug discovery tools." Nature Biotechnology 26, no. 9 (September 2008): 960–61. http://dx.doi.org/10.1038/nbt0908-960.

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43

MCNEISH, J. "Stem cells as screening tools in drug discovery." Current Opinion in Pharmacology 7, no. 5 (October 2007): 515–20. http://dx.doi.org/10.1016/j.coph.2007.06.005.

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Auberson, Yves P. "In vivo molecular imaging tools facilitate drug discovery." Drug Discovery Today: Technologies 25 (November 2017): 1–2. http://dx.doi.org/10.1016/j.ddtec.2017.11.010.

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Lee, Hyeong-Min, Patrick M. Giguere, and Bryan L. Roth. "DREADDs: novel tools for drug discovery and development." Drug Discovery Today 19, no. 4 (April 2014): 469–73. http://dx.doi.org/10.1016/j.drudis.2013.10.018.

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Marsden, Catherine J., Sonia Eckersley, Max Hebditch, Alexander J. Kvist, Roy Milner, Danielle Mitchell, Juli Warwicker, and Anna E. Marley. "The Use of Antibodies in Small-Molecule Drug Discovery." Journal of Biomolecular Screening 19, no. 6 (April 2, 2014): 829–38. http://dx.doi.org/10.1177/1087057114527770.

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Анотація:

Antibodies are powerful research tools that can be used in many areas of biology to probe, measure, and perturb various biological structures. Successful drug discovery is dependent on the correct identification of a target implicated in disease, coupled with the successful selection, optimization, and development of a candidate drug. Because of their specific binding characteristics, with regard to specificity, affinity, and avidity, coupled with their amenability to protein engineering, antibodies have become a key tool in drug discovery, enabling the quantification, localization, and modulation of proteins of interest. This review summarizes the application of antibodies and other protein affinity reagents as specific research tools within the drug discovery process.

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Eze, S. C., M. Isioma, C. C. Ugorji, and G. O. Ozota. "Novel Antimalarial Drug Targets as Potent Tools to Accelerate Drug Discovery: A Short Review." Journal of Basic and Social Pharmacy Research 2, no. 5 (2022): 21–29. http://dx.doi.org/10.52968/27455385.

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Introduction: Malaria is a significant tropical disease and the greatest killer of all time. The molecular pathways of known antimalarial drugs have been extensively elucidated. However, the emergence of resistant plasmodium species, especially that of P. falciparum, further threatens the prospects of its eradication. The advancement in proteomics and genomics has taken us a step further. Mere serendipity and pharmacology-based approaches can no longer take the lead in drug discovery. Newer and better antimalarial drug targets need to be sought. Objectives: This study presents the need and problems in identifying and validating novel antimalarial drug targets to accelerate drug discovery. Methods: Relevant literature was retrieved from Google Scholar, PubMed, and ScienceDirect. An exploratory search for traditional antimalarial drug targets and their shortcomings were reviewed, and the problems in identifying and validating novel drug targets. Possible solutions were proposed. Body: Emerging resistance and advances in proteomics drive the need for newer targets. Significant problems include the lack of crystal structure of some targets and determining the essentiality of genes and their cognate proteins. The in-silico approach using phylogenetic comparison can quickly determine the essentiality of genes, and Protein Interference Assay (PIA) is potent in validating newer targets. Conclusion: Identifying and validating novel antimalarial drug targets will effectively drive the search for and discovery of newer drugs.

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Mohamed Abd El-Aziz, Garcia Soares, and Stockand. "Snake Venoms in Drug Discovery: Valuable Therapeutic Tools for Life Saving." Toxins 11, no. 10 (September 25, 2019): 564. http://dx.doi.org/10.3390/toxins11100564.

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Animal venoms are used as defense mechanisms or to immobilize and digest prey. In fact, venoms are complex mixtures of enzymatic and non-enzymatic components with specific pathophysiological functions. Peptide toxins isolated from animal venoms target mainly ion channels, membrane receptors and components of the hemostatic system with high selectivity and affinity. The present review shows an up-to-date survey on the pharmacology of snake-venom bioactive components and evaluates their therapeutic perspectives against a wide range of pathophysiological conditions. Snake venoms have also been used as medical tools for thousands of years especially in tradition Chinese medicine. Consequently, snake venoms can be considered as mini-drug libraries in which each drug is pharmacologically active. However, less than 0.01% of these toxins have been identified and characterized. For instance, Captopril® (Enalapril), Integrilin® (Eptifibatide) and Aggrastat® (Tirofiban) are drugs based on snake venoms, which have been approved by the FDA. In addition to these approved drugs, many other snake venom components are now involved in preclinical or clinical trials for a variety of therapeutic applications. These examples show that snake venoms can be a valuable source of new principle components in drug discovery.

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Batool, Maria, Bilal Ahmad, and Sangdun Choi. "A Structure-Based Drug Discovery Paradigm." International Journal of Molecular Sciences 20, no. 11 (June 6, 2019): 2783. http://dx.doi.org/10.3390/ijms20112783.

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Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

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Bajorath, Jürgen. "Computer-aided drug discovery." F1000Research 4 (August 26, 2015): 630. http://dx.doi.org/10.12688/f1000research.6653.1.

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Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

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