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Journal articles on the topic 'Computational aided drug discovery'

Author: Grafiati

Published: 9 March 2023

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1

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. Approach

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Sharma, Anu, Lalubhai Jangid, Nusrat Shaikh, and Jitendra Bhangale. "Computer-Aided Drug Design Boon in Drug Discovery." Asian Journal of Organic & Medicinal Chemistry 7, no. 1 (2022): 55–64. http://dx.doi.org/10.14233/ajomc.2022.ajomc-p361.

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An innovative sequential step of detecting new medicines or drugs dependent on the information of a target is called drug design. The drug is a small molecule that alters the capacity of a bimolecular, example, protein, receptor or catalyst that leads to restorative incentive for patients. Designing of drug by computational method helped steady use of computational science to find, improve and study drugs as well as biologically related active molecules. The displaying examines like the structure-based plan; ligand-based drugs structure; database looking and restricting partiality dependent on

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Richards, W. Graham. "Computer-aided drug discovery." Proceedings of the Royal Society of Edinburgh. Section B. Biological Sciences 99, no. 1-2 (1992): 105–11. http://dx.doi.org/10.1017/s0269727000013087.

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Synopsis:The role of computers in drug discovery depends on just how much is known about the target macromolecule. If atomic detail of the receptor is known, binding free energy differences between drug variants may be computed. Major effort is being expended in extending the area of applicability of such studies by predicting protein structure based on homologies with known protein crystal data. Where no target structure is available, computational methods can provide leads by defining transition state structures and then using the approach of molecular similarity to define stable mimics to a

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KIRBOĞA, Kevser Kübra, and Ecir KÜÇÜKSİLLE. "Bilgisayar Destekli İlaç Keşfi Üzerine Bakışlar." Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi 11, no. 2 (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

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Surabhi, Surabhi, and BK Singh. "COMPUTER AIDED DRUG DESIGN: AN OVERVIEW." Journal of Drug Delivery and Therapeutics 8, no. 5 (2018): 504–9. http://dx.doi.org/10.22270/jddt.v8i5.1894.

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Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to v

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Nero, Tracy L., Michael W. Parker, and Craig J. Morton. "Protein structure and computational drug discovery." Biochemical Society Transactions 46, no. 5 (2018): 1367–79. http://dx.doi.org/10.1042/bst20180202.

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The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a w

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Sehgal, Vijay Kumar, Supratik Das, and Anand Vardhan. "Computer Aided Drug Designing." International Journal of Medical and Dental Sciences 6, no. 1 (2017): 1433. http://dx.doi.org/10.18311/ijmds/2017/18804.

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Designing of drugs and their development are a time and resource consuming process. There is an increasing effort to introduce the role of computational approach to chemical and biological space in order to organise the design and development of drugs and their optimisation. The role of Computer Aided Drug Designing (CADD) are nowadays expressed in Nanotechnology, Molecular biology, Biochemistry etc. It is a diverse discipline where various forms of applied and basic researches are interlinked with each other. Computer aided or in Silico drug designing is required to detect hits and leads. Opt

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Ejalonibu, Murtala A., Segun A. Ogundare, Ahmed A. Elrashedy, et al. "Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach." International Journal of Molecular Sciences 22, no. 24 (2021): 13259. http://dx.doi.org/10.3390/ijms222413259.

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Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced th

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Bajorath, Jürgen. "Computational chemistry and computer-aided drug discovery: Part II." Future Medicinal Chemistry 8, no. 15 (2016): 1799–800. http://dx.doi.org/10.4155/fmc-2013-0123.

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Bajorath, Jürgen. "Computational chemistry and computer-aided drug discovery: Part I." Future Medicinal Chemistry 8, no. 14 (2016): 1705–6. http://dx.doi.org/10.4155/fmc-2016-0264.

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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 (2022): 173–79. http://dx.doi.org/10.25303/2608rjce1730179.

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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 involve

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Ain, Qurat ul, Maria Batool, and Sangdun Choi. "TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches." Molecules 25, no. 3 (2020): 627. http://dx.doi.org/10.3390/molecules25030627.

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The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors

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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 ha

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Wang, Ge, Yuhao Bai, Jiarui Cui, Zirui Zong, Yuan Gao, and Zhen Zheng. "Computer-Aided Drug Design Boosts RAS Inhibitor Discovery." Molecules 27, no. 17 (2022): 5710. http://dx.doi.org/10.3390/molecules27175710.

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The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signal

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Paiman, Arif, Ahmad Mohammad, and Mubashar Rehman. "Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction." Global Drug Design & Development Review II, no. I (2017): 1–8. http://dx.doi.org/10.31703/gdddr.2017(ii-i).01.

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In modern day, Data on different diseases and drug substances with their properties like modification, side effects, and dose requires documentation data and building library exploring, such library with vast information in every aspect needs computational methods used in CADD. Recognition of specific targets for the drug tested and defining pharmacological activity of a drug candidate based on the structure of both drug and its target, finding outside effects of drugs at the molecular level and calculation of toxicity caused by metabolism of drug applications of Computer aided drug design in

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Sachin S Padole, Alpana J Asnani, Dinesh R Chaple, and Soumya G Katre. "A review of approaches in computer-aided drug design in drug discovery." GSC Biological and Pharmaceutical Sciences 19, no. 2 (2022): 075–83. http://dx.doi.org/10.30574/gscbps.2022.19.2.0161.

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The process of discovering and developing a new medication is often seen as a lengthy and expensive endeavors. As a result, computer-aided drug design methods are now frequently utilized to improve the efficiency of the drug discovery and development process. Various CADD approaches are regarded as potential techniques based on their needs; nevertheless, structure-based drug design and ligand-based drug design approaches are well-known as highly efficient and powerful strategies in drug discovery and development. Both of these approaches may be used in conjunction with molecular docking to con

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Karaman, Berin, and Wolfgang Sippl. "Computational Drug Repurposing: Current Trends." Current Medicinal Chemistry 26, no. 28 (2019): 5389–409. http://dx.doi.org/10.2174/0929867325666180530100332.

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: Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre

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Zhang, Yue, Mengqi Luo, Peng Wu, Song Wu, Tzong-Yi Lee, and Chen Bai. "Application of Computational Biology and Artificial Intelligence in Drug Design." International Journal of Molecular Sciences 23, no. 21 (2022): 13568. http://dx.doi.org/10.3390/ijms232113568.

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Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable comp

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Ahirwar, Hemant, Gabbar Kurmi, Rubeena Khan, et al. "Review on QSAR using Anticancer Drug." Asian Journal of Dental and Health Sciences 2, no. 4 (2022): 59–63. http://dx.doi.org/10.22270/ajdhs.v2i4.27.

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New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology for faster, cheaper and more effective drug design. Recently, the rapid growth

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Ramesh, Muthusamy, and Arunachalam Muthuraman. "Computer-Aided Drug Discovery (CADD) Approaches for the Management of Neuropathic Pain." Current Topics in Medicinal Chemistry 21, no. 32 (2021): 2856–68. http://dx.doi.org/10.2174/1568026621666211122161932.

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Neuropathic pain occurs due to physical damage, injury, or dysfunction of neuronal fibers. The pathophysiology of neuropathic pain is too complex. Therefore, an accurate and reliable prediction of the appropriate hits/ligands for the treatment of neuropathic pain is a challenging process. However, computer-aided drug discovery approaches contributed significantly to discovering newer hits/ligands for the treatment of neuropathic pain. The computational approaches like homology modeling, induced-fit molecular docking, structure-activity relationships, metadynamics, and virtual screening were ci

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De, Baishakhi, Koushik Bhandari, Francisco J. B. Mendonça, Marcus T. Scotti, and Luciana Scotti. "Computational Studies in Drug Design Against Cancer." Anti-Cancer Agents in Medicinal Chemistry 19, no. 5 (2019): 587–91. http://dx.doi.org/10.2174/1871520618666180911125700.

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Background: The application of in silico tools in the development of anti cancer drugs. Objective: The summing of different computer aided drug design approaches that have been applied in the development of anti cancer drugs. Methods: Structure based, ligand based, hybrid protein-ligand pharmacophore methods, Homology modeling, molecular docking aids in different steps of drug discovery pipeline with considerable saving in time and expenditure. In silico tools also find applications in the domain of cancer drug development. Results: Structure-based pharmacophore modeling aided in the identific

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Jaiyeoba, Oluwaseyi, Azman Samsudin, and Suhaila Sulaiman. "Utilising the Computational Power of Blockchain Proof-ofWork in Computer-Aided Drug Design." International Journal of Emerging Technology and Advanced Engineering 12, no. 10 (2022): 37–50. http://dx.doi.org/10.46338/ijetae1022_05.

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Proof-of-Work (PoW) algorithm is a popular blockchain algorithm employed in many blockchain applications such as Bitcoin. Cryptographic hashing is the foundation of the PoW algorithm and blockchain technology in general. Unfortunately, the use of hashing in PoW has led to huge computational requirements. Researchers and industrialists are aware of the immense energy consumed by the PoW algorithm in blockchain-based cryptocurrencies. For instance, Bitcoin currently consumes above 110 TWh of electricity annually. This vast amount of energy is used to calculate non-valuable cryptographic hashes,

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Kumar, Sanjiv. "ROLE OF COMPUTER-AIDED DRUG DESIGN IN THE DISCOVERY AND DEVELOPMENT OF NEW MEDICINAL AGENTS A REVIEW." Journal of medical pharmaceutical and allied sciences 11, no. 3 (2022): 4794–801. http://dx.doi.org/10.55522/jmpas.v11i3.2300.

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Drug design and development is a time consuming and costly process. Nowadays, computer-aided drug design approaches are usually used to improve drug discovery and advancement efficiency. The role of Computer-Aided Drug Design (CADD) is a diverse discipline in which various versions of applied and basic analysis are interlinked. It is being implemented in various fields including biochemistry, molecular biology, nanotechnology etc. Various employed computational approaches includes ligand-based drug design, structure-based drug design, quantitative structure-property relationships and quantitat

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Gurung, Arun Bahadur, Mohammad Ajmal Ali, Joongku Lee, Mohammad Abul Farah, and Khalid Mashay Al-Anazi. "An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19." BioMed Research International 2021 (June 24, 2021): 1–18. http://dx.doi.org/10.1155/2021/8853056.

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The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved

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Rakesh, Palepu Narasimha. "A Data Science Approach to Bioinformatics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3860–69. http://dx.doi.org/10.22214/ijraset.2021.37221.

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Computer aided drug design (CADD) which uses the computational advance towards to develop, discover and scrutinize and examine drugs and alike biologically agile molecules. CADD is a specialized stream which uses the computational techniques to mimic drug-receptor interactions. CADD procedures are so much dependent on the tools of bioinformatics, databases & applications. There are so many advantages of computer aided drug discovery; it saves lot of time which is one of the main advantages followed by low cost and more accuracy. CADD required less manpower to work. There are different type

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Mizera, Mikołaj, Eugene N. Muratov, Vinicius M. Alves, Alexander Tropsha, and Judyta Cielecka-Piontek. "Computer-Aided Discovery of New Solubility-Enhancing Drug Delivery System." Biomolecules 10, no. 6 (2020): 913. http://dx.doi.org/10.3390/biom10060913.

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The poor aqueous solubility of active pharmaceutical ingredients (APIs) places a limit on their therapeutic potential. Cyclodextrins (CDs) have been shown to improve the solubility of APIs, but the magnitude of the improvement depends on the structure of both the CDs and APIs. We have developed quantitative structure–property relationship (QSPR) models that predict the stability of the complexes formed by a popular poorly soluble antibiotic, cefuroxime axetil (CA) and different CDs. We applied this model to five CA–CD systems not included in the modeling set. Two out of three systems predicted

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Almasri, Ihab M. "Computational approaches for the discovery of natural pancreatic lipase inhibitors as antiobesity agents." Future Medicinal Chemistry 12, no. 8 (2020): 741–57. http://dx.doi.org/10.4155/fmc-2019-0284.

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Obesity is becoming one of the greatest threats to global health in the 21st century and therefore the development of novel antiobesity drugs is one of the top priorities of global drug research. An important treatment strategy includes the reduction of intestinal fat absorption through the inhibition of pancreatic lipase (PL). Natural products provide a vast pool of PL inhibitors with novel scaffolds that can possibly be developed into clinical products. Computational drug design methods have become increasingly invaluable in the drug discovery process. In recent years, the discovery of new a

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Chien, Shang-Tao, Ajay Kumar, Shifa Pandey, et al. "Cancer Biology Aspects of Computational Methods & Applications in Drug Discovery." Current Pharmaceutical Design 24, no. 32 (2019): 3758–66. http://dx.doi.org/10.2174/1381612824666181112104921.

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Background: Cancer is one of the most debilitating diseases worldwide; even though advances in molecular and cellular biology have contributed to the decline of mortality associated with cancer, the procedure of drug discovery and development of cancer are time-consuming and expensive. However, with computer-aided drug discovery (CADD) techniques, pharmaceutical firms can save production costs and reduce the time of introducing effective anticancer drugs for clinical trials. CADD strategies like structure-based drug designing, ligandbased drug designing, and combined structure-based and ligand

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Patel, Jimish R., Hirak V. Joshi, Ujashkumar A. Shah, and Jayvadan K. Patel. "A Review on Computational Software Tools for Drug Design and Discovery." Indo Global Journal of Pharmaceutical Sciences 12 (2022): 53–81. http://dx.doi.org/10.35652/igjps.2022.12006.

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In the current era of modern drug design & development via computer-aided drug design, the potential role of computational software tools is widely enlarged in use. Computer-based drug design is revolutionary in the new drug discovery process because these processes are fast, time, and cost-saving with more efficient pharmacological activity. Computer-Based drug design is mainly applied for the drug-design and gets many successes in new drug research. There is plenty of software available in drug design; however; still, many issues are rising during its use. To clarify these issues, an att

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PHAM, Quan Minh, and Long Quoc PHAM. "VIRTUAL SCREENING STATEGIES IN DRUG DISCOVERY – A BRIEF OVERVIEW." Vietnam Journal of Science and Technology 59, no. 4 (2021): 415. http://dx.doi.org/10.15625/2525-2518/59/4/16003.

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Computer-aided drug design has now become a compulsory tool in the drug discovery and development process which uses computational approaches to discover potential compounds with expected biological activities. Firstly, this review provides a comprehensive introduction of the virtual screening technique, knowledge and advances in both SBVS and LBVS strategies also presented. Secondly, recent database of compounds provided worldwide and drug-like parameters which are helpful in supporting the VS process will be discussed. These information will provides a good platform to estimate the advance o

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Bajorath, Jürgen. "Pushing the boundaries of computational approaches: special focus issue on computational chemistry and computer-aided drug discovery." Future Medicinal Chemistry 7, no. 18 (2015): 2415–17. http://dx.doi.org/10.4155/fmc.15.157.

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Makhouri, Farahnaz R., and Jahan B. Ghasemi. "Combating Diseases with Computational Strategies Used for Drug Design and Discovery." Current Topics in Medicinal Chemistry 18, no. 32 (2019): 2743–73. http://dx.doi.org/10.2174/1568026619666190121125106.

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Computer-aided drug discovery (CADD) tools have provided an effective way in the drug discovery pipeline for expediting of this long process and economizing the cost of research and development. Due to the dramatic increase in the availability of human proteins as drug targets and small molecule information due to the advances in bioinformatics, cheminformatics, genomics, proteomics, and structural information, the applicability of in silico drug discovery has been extended. Computational approaches have been used at almost all stages in the drug discovery pipeline including target identificat

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Nayarisseri, Anuraj. "Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery." Current Topics in Medicinal Chemistry 20, no. 19 (2020): 1651–60. http://dx.doi.org/10.2174/156802662019200701164759.

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Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the majo

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Jakhar, Ritu, Mehak Dangi, Alka Khichi, and Anil Kumar Chhillar. "Relevance of Molecular Docking Studies in Drug Designing." Current Bioinformatics 15, no. 4 (2020): 270–78. http://dx.doi.org/10.2174/1574893615666191219094216.

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Molecular Docking is used to positioning the computer-generated 3D structure of small ligands into a receptor structure in a variety of orientations, conformations and positions. This method is useful in drug discovery and medicinal chemistry providing insights into molecular recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery (CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment of targets and ligand. Over the last decade, advances in the field of computational, proteomics and genomics have also led to the de

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Zhu, Siyu, Meixian Wu, Ziwei Huang, and Jing An. "Trends in application of advancing computational approaches in GPCR ligand discovery." Experimental Biology and Medicine 246, no. 9 (2021): 1011–24. http://dx.doi.org/10.1177/1535370221993422.

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G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological str

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Rahman, Md Mominur, Md Rezaul Islam, Firoza Rahman, et al. "Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance." Bioengineering 9, no. 8 (2022): 335. http://dx.doi.org/10.3390/bioengineering9080335.

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Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols

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Chong, Li Chuin, Gayatri Gandhi, Jian Ming Lee, Wendy Wai Yeng Yeo, and Sy-Bing Choi. "Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review." International Journal of Molecular Sciences 22, no. 16 (2021): 8962. http://dx.doi.org/10.3390/ijms22168962.

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Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, ther

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Panwar, Umesh, Ishwar Chandra, Chandrabose Selvaraj, and Sanjeev K. Singh. "Current Computational Approaches for the Development of Anti-HIV Inhibitors: An Overview." Current Pharmaceutical Design 25, no. 31 (2019): 3390–405. http://dx.doi.org/10.2174/1381612825666190911160244.

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Background: Today, HIV-1 infection has become an extensive problem to public health and a greater challenge to all working researchers throughout the world. Since the beginning of HIV-1 virus, several antiviral therapeutic agents have been developed at various stages to combat HIV-1 infection. But, many of antiviral drugs are on the platform of drug resistance and toxicology issues, needs an urgent constructive investigation for the development of productive and protective therapeutics to make an improvement of individual life suffering with viral infection. As developing a novel agent is very

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Poli, Giulio, and Tiziano Tuccinardi. "Consensus Docking in Drug Discovery." Current Bioactive Compounds 16, no. 3 (2020): 182–90. http://dx.doi.org/10.2174/1573407214666181023114820.

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Background: Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of

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Rondon-Villarreal, Paola, and Efrain Pinzon-Reyes. "Computer Aided Design of Non-toxic Antibacterial Peptides." Current Topics in Medicinal Chemistry 18, no. 13 (2018): 1044–52. http://dx.doi.org/10.2174/1568026618666180719163251.

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Antimicrobial resistance is increasing at an alarming rate and the number of new antibiotics developed and approved has decreased in the last decades, basically for economic and regulatory obstacles. Pathogenic bacteria that are resistant to multiple or all available antibiotics are isolated frequently. Hence, new antibacterial agents are urgently needed and antimicrobial peptides are being considered as a potential solution to this important threat. These molecules are small host defense proteins that are part of the immune systems of most living organisms such as plants, bacteria, invertebra

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Sarkar, Sharanya, Khushboo Gulati, Manikyaprabhu Kairamkonda, Amit Mishra, and Krishna Mohan Poluri. "Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases." Current Topics in Medicinal Chemistry 18, no. 20 (2018): 1719–36. http://dx.doi.org/10.2174/1568026618666181025114903.

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Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule

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Vucicevic, Jelica, Katarina Nikolic, and John B. O. Mitchell. "Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches." Current Medicinal Chemistry 26, no. 21 (2019): 3874–89. http://dx.doi.org/10.2174/0929867324666170712115411.

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Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increas

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Dar, Khalid Bashir, Aashiq Hussain Bhat, Shajrul Amin, et al. "Modern Computational Strategies for Designing Drugs to Curb Human Diseases: A Prospect." Current Topics in Medicinal Chemistry 18, no. 31 (2019): 2702–19. http://dx.doi.org/10.2174/1568026619666190119150741.

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Drug discovery is an exhaustive and time-consuming process involving numerous stages like target identification, validation, lead optimization, preclinical trials, clinical trials and finally postmarketing vigilance for drug safety. The application of computer-aided drug designing (CADD) is an indispensable approach for developing safe and effective drugs. Previous methods based on combinatorial chemistry (CC) and high throughput screening (HTS) consumed a lot of time as well as expenditure. CADD based approaches including pharmacophore modeling (PM), molecular docking (MD), inverse docking, c

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Pérez-Regidor, Lucía, Joan Guzmán-Caldentey, Nils Oberhauser, et al. "Small Molecules as Toll-like Receptor 4 Modulators Drug and In-House Computational Repurposing." Biomedicines 10, no. 9 (2022): 2326. http://dx.doi.org/10.3390/biomedicines10092326.

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The innate immunity toll-like receptor 4 (TLR4) system is a receptor of paramount importance as a therapeutic target. Virtual screening following a “computer-aided drug repurposing” approach was applied to the discovery of novel TLR4 modulators with a non-lipopolysaccharide-like structure. We screened almost 29,000 approved drugs and drug-like molecules from commercial, public, and in-house academia chemical libraries and, after biological assays, identified several compounds with TLR4 antagonist activity. Our computational protocol showed to be a robust approach for the identification of hits

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Manjunath, Meghna, and Sinosh Skariyachan. "Screening of Natural Lead Molecules Against Putative Molecular Targets of Drug-resistant Cryptococcus spp: An Insight from Computer-aided Molecular Design." Current Topics in Medicinal Chemistry 18, no. 31 (2019): 2681–701. http://dx.doi.org/10.2174/1568026619666190119145434.

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Cryptococcosis is one of the major invasive fungal infections distributed worldwide with high mortality rate. C. neoformans and C. gattii are the major organisms that cause various types of infections. Anti-fungal resistances exhibited by the mentioned species of Cryptococcus threaten their effective prevention and treatment. There is limited information available on human to human transmission of the pathogen and virulent factors that are responsible for Cryptococcus mediated infections. Hence, there is high scope for understanding the mechanism, probable drug targets and scope of developing

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Pavan, Matteo, and Stefano Moro. "Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics." International Journal of Molecular Sciences 24, no. 5 (2023): 4401. http://dx.doi.org/10.3390/ijms24054401.

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Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active mo

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Hsieh, Chia-Ju, Sam Giannakoulias, E. James Petersson, and Robert H. Mach. "Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development." Pharmaceuticals 16, no. 2 (2023): 317. http://dx.doi.org/10.3390/ph16020317.

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The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates t

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Viana, Jéssika O., Marcus T. Scotti, and Luciana Scotti. "Computer-aided Drug Design Investigations for Benzothiazinone Derivatives Against Tuberculosis." Combinatorial Chemistry & High Throughput Screening 23, no. 1 (2020): 66–82. http://dx.doi.org/10.2174/1386207323666200117102316.

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Background: Tuberculosis (Mycobacterium tuberculosis) is an infectious bacterial disease with the highest levels of mortality worldwide, presenting numerous cases of resistance. In silico studies, which elaborate chemical and biological models in computational tools and make it possible to interpret molecular characteristics, are among the methods used in the search for new drugs. Objective: In this perspective, our aim was to use QSAR and molecular modeling to propose possible pharmacophores from benzothiazinone derivatives. Methods: In this study, a set of 69 benzothiazinone derivatives, tog

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Floresta, Giuseppe, Davide Gentile, Giancarlo Perrini, Vincenzo Patamia, and Antonio Rescifina. "Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach." Marine Drugs 17, no. 11 (2019): 624. http://dx.doi.org/10.3390/md17110624.

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Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have received interest following the recent publication of their pharmacologically beneficial effects. Recently, it was revealed that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other metabolic diseases, and some type of cancers. In past years, hundreds of effective FABP4 inhibitors have been synthesized and discovered, but, unfortunately, none have reached the clinical research phase. The field of computer-aided drug design seems to be promising and useful for the identification of FA

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Rudrapal, Mithun, and Dipak Chetia. "Virtual Screening, Molecular Docking and QSAR Studies in Drug Discovery and Development Programme." Journal of Drug Delivery and Therapeutics 10, no. 4 (2020): 225–33. http://dx.doi.org/10.22270/jddt.v10i4.4218.

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Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two basic approaches of computer-aided drug design (CADD) used in modern drug discovery and development programme. Virtual screening (or in silico screening) has been used in drug discovery program as a complementary tool to high throughput screening (HTS) to identify bioactive compounds. It is a preliminary tool of CADD that has gained considerable interest in the pharmaceutical research as a productive and cost-effective technology in search for novel molecules of medicinal interest. Docking is also used for virtu

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