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Journal articles on the topic 'Drug discovery'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 March 2025

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1

Aziz Ahmad, Kashif, Saleha Akram Nizami, and Muhammad Haroon Ghous. "Coronavirus - Drug Discovery and Therapeutic Drug Monitoring Options." Pharmaceutics and Pharmacology Research 5, no. 2 (January 6, 2022): 01–04. http://dx.doi.org/10.31579/2693-7247/044.

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COVID-19 is basically a medium size RNA virus and the nucleic acid is about 30 kb long, positive in sense, single stranded and polyadenylated. The RNA which is found in this virus is the largest known RNA and codes for a large polyprotein. In addition, coronaviruses are capable of genetic recombination if 2 viruses infect the same cell at the same time. SARS-CoV emerged first in southern China and rapidly spread around the globe in 2002–2003. In November 2002, an unusual epidemic of atypical pneumonia with a high rate of nosocomial transmission to health-care workers occurred in Foshan, Guangdong, China. In March 2003, a novel CoV was confirmed to be the causative agent for SARS, and was thus named SARS-CoV. Despite the report of a large number of virus-based and host-based treatment options with potent in vitro activities for SARS and MERS, only a few are likely to fulfil their potential in the clinical setting in the foreseeable future. Most drugs have one or more major limitations that prevent them from proceeding beyond the in vitro stage. First, many drugs have high EC50/Cmax ratios at clinically relevant dosages

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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 (July 25, 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 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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Jadhav, Mr Gahininath Thansing, and Mr Rahul Bhavlal Jadhav. "Drug Discovery and Development Process." International Journal of Research Publication and Reviews 5, no. 1 (January 8, 2024): 1891–95. http://dx.doi.org/10.55248/gengpi.5.0124.0225.

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Sharma, Bhavik. "DRUG DISCOVERY AND DEVELOPMENT: AN OVERVIEW." INDIAN RESEARCH JOURNAL OF PHARMACY AND SCIENCE 7, no. 2 (June 2020): 2215–26. http://dx.doi.org/10.21276/irjps.2020.7.2.14.

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Siddharthan, N., M. Raja Prabu, and B. Sivasankari. "Bioinformatics in Drug Discovery a Revi." International Journal of Research in Arts and Science 2, no. 2 (April 30, 2016): 11–13. http://dx.doi.org/10.9756/ijras.8099.

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Parkhill, Susannah L., and Eachan O. Johnson. "Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery." Biochemical Journal 481, no. 13 (July 3, 2024): 839–64. http://dx.doi.org/10.1042/bcj20220062.

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The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs — a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

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Alehaideb, Zeyad, Nimer Mehyar, Mai Al Ajaji, Mohammed Alassiri, Manal Alaamery, Bader Al Debasi, Bandar Alghanem, et al. "KAIMRC’S Second Therapeutics Discovery Conference." Proceedings 43, no. 1 (April 29, 2020): 6. http://dx.doi.org/10.3390/proceedings2020043006.

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Following the success of our first therapeutic discovery conference in 2017 and the selection of King Abdullah International Medical Research Centre (KAIMRC) as the first Phase 1 clinical site in the Kingdom of Saudi Arabia, we organized our second conference in partnership with leading institutions in academic drug discovery, which included the Structural Genomic Constorium (Oxford, UK), Fraunhofer (Germany) and Institute Material Medica (China); the participation of members of the American Drug Discovery Consterium; European Biotech companies; and local pharma companies, SIPMACO and SudairPharma. In addition, we had European and Northern American venture capital experts attending and presenting at the conference. The purpose of the conference was to bridge the gap between biotech, pharma and academia regarding drug discovery and development. Its aim primarily was to: (a) bring together world experts on academic drug discovery to discuss and propose new approaches to discover and develop new therapies; (b) establish a permanent platform for scientific exchange between academia and the biotech and pharmaceutical industries; (c) entice national and international investors to consider funding drugs discovered in academia; (d) educate the population about the causes of diseases, approaches to prevent them from happening and their cure; (e) attract talent to consider the drug discovery track for their studies and career. During the conference, we discussed the unique academic drug discovery disrupting business models, which can make their discoveries easily accessible in an open source mode. This unique model accelerates the dissemination of knowledge to all world scientists to guide them in their research. This model is aimed at bringing effective and affordable medicine to all mankind in a very short time. Moreover, the program discussed rare disease targets, orphan drug discovery, immunotherapy discovery and process, the role of bioinformatics in drug discovery, anti-infective drug discovery in the era of bad bugs, natural products as a source of novel drugs and innovative drug formulation and delivery. Additionally, as the conference was organized during the surge of the epidemic, we dedicated the first day (25 February) to coronavirus science, detection and therapy. The day was co-organized with the King Saud bin Abdulaziz University for Health Sciences, Kingdom of Saudi Arabia(KSA) Ministry of Education to announce the grant winner for infectious diseases. Simultaneously, intensive courses were delivered to junior scientists on the principle of drug discovery, immunology and clinical trials, as well as rare diseases. The second therapeutics discovery forum provided a platform for interactive knowledge sharing and the convergence of researchers, governments, pharmaceuticals, biopharmaceuticals, hospitals and non-profit organizations on the topic of academic drug discovery. The event presented showcases on global drug discovery initiatives and demonstrated how collaborations are leading to successful new therapies. In line with the KSA 2030 vision on becoming world leaders with an innovative economy and healthy population, therapeutic discovery is becoming an area of interest to science leaders in the kingdom, and our conference gave us the opportunity to identity key areas of interest as well as potential future collaborations.

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Antonelli, Alessandro. "Drug Discovery." Current Pharmaceutical Design 28, no. 3 (January 2022): 179. http://dx.doi.org/10.2174/1381612828666220103172626.

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9

Kennedy, D. "Drug Discovery." Science 303, no. 5665 (March 19, 2004): 1729. http://dx.doi.org/10.1126/science.303.5665.1729.

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MULLIN, RICK. "DRUG DISCOVERY." Chemical & Engineering News Archive 82, no. 30 (July 26, 2004): 23–32. http://dx.doi.org/10.1021/cen-v082n030.p023.

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BRENNAN, MAIRIN B. "Drug Discovery." Chemical & Engineering News 78, no. 23 (June 5, 2000): 63–73. http://dx.doi.org/10.1021/cen-v078n023.p063.

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MULLIN, RICK. "DRUG DISCOVERY." Chemical & Engineering News Archive 81, no. 30 (July 28, 2003): 21–31. http://dx.doi.org/10.1021/cen-v081n030.p021.

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13

Pinnock, Rob. "Drug discovery." New Scientist 194, no. 2600 (April 2007): 20. http://dx.doi.org/10.1016/s0262-4079(07)60976-2.

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Nikolova, Stoyanka. "Drug Discovery." Applied Sciences 13, no. 22 (November 16, 2023): 12378. http://dx.doi.org/10.3390/app132212378.

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Glew, Robert H. "Drug discovery and development, Vol. 1: Drug discovery." Biochemistry and Molecular Biology Education 35, no. 2 (2007): 162. http://dx.doi.org/10.1002/bmb.38.

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16

Chopra, Hitesh, Sandeep Kumar, Vandana ., and Sandeep Arora. "Pharmacogenomics: Applications in Drug Discovery and Pharmacotherapy." Journal of Pharmaceutical Technology, Research and Management 2, no. 1 (May 5, 2014): 47–60. http://dx.doi.org/10.15415/jptrm.2014.21004.

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17

Badola, Ashutosh, Sakshi Negi, and Preeti Kothiyal. "Bioanalysis: An Important Tool in Drug Discovery." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1273–79. http://dx.doi.org/10.31142/ijtsrd14187.

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18

Ahmed, Manal Hatem, Saja Ismail Karkush, Sumeia Abbas Ali, and Ali Abdulmawjood Mohammed. "Phytochemicals: a new arsenal in drug discovery." International Journal of Medical Science and Dental Health 10, no. 01 (January 1, 2024): 29–44. http://dx.doi.org/10.55640/ijmsdh-10-01-03.

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In ancient times traditional herbs were used to treat different diseases such as stomach discomfort, toothache, body pain and inflammation, diarrhea, malaria, typhoid, diabetes, and so on. Medicinally important plants are recognized to have chemicals or phytochemicals that could be useful for illness treatment or medication manufacture. These compounds occur naturally in plant parts (leaves, stems, barks, and roots) and are referred to as secondary metabolites because, like primary metabolites, they are synthesized to protect the plant rather than for growth. Fortunately for humans, the majority of these secondary metabolites have therapeutic properties that are useful against a variety of diseases and health problems. Resistance to antibiotics is one of the world's most critical health challenges, with numerous infections rapidly gaining resistance to conventional antimicrobials. There is currently no viable therapeutic agent with the ability to reverse antimicrobial resistance, and several leading laboratories are working hard to find new antimicrobials. Plant-based chemical compounds have received comparatively little attention in the context of antimicrobial medication development. Natural chemicals have piqued the interest of drug development scientists because of their structural diversity, chemical novelty, abundance, and bioactivity. Cancer is currently a major problem. Despite the numerous interventions available, a huge number of patients die each year as a result of cancer disorders. The rising research direction in healthcare pharmacy is the development of an effective and side-effect-free anticancer medication. Chemical entities found in plants have proven to be particularly promising in this area. Bioactive phytochemicals are preferred because they act differentially on cancer cells while leaving normal cells alone. This review provides an overview of the utility of medicinal plants as well as secondary metabolites of plants as drug sources, the drug discovery process, the efficacy and safety of phytochemicals, current applications, developments in screening technologies, challenges, and future directions.

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Bhusare, Shubham, Ms Dipmala Ghorpade, and Dr Gajanan Sanap. "Artificial Intelligence in Drug Discovery and Development." International Journal of Research Publication and Reviews 6, no. 1 (January 2025): 1096–106. https://doi.org/10.55248/gengpi.6.0125.0307.

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20

Li, Juan, Ranjana Sharma, and Yan Bai. "Discovering Complex Relationships of Drugs over Distributed Knowledgebases." International Journal of Distributed Systems and Technologies 5, no. 1 (January 2014): 22–39. http://dx.doi.org/10.4018/ijdst.2014010102.

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Drug discovery is a lengthy, expensive and difficult process. Indentifying and understanding the hidden relationships among drugs, genes, proteins, and diseases will expedite the process of drug discovery. In this paper, we propose an effective methodology to discover drug-related semantic relationships over large-scale distributed web data in medicine, pharmacology and biotechnology. By utilizing semantic web and distributed system technologies, we developed a novel hierarchical knowledge abstraction and an efficient relation discovery protocol. Our approach effectively facilitates the realization of the full potential of harnessing the collective power and utilization of the drug-related knowledge scattered over the Internet.

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21

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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22

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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JIMÉNEZ-DÍAZ, MARÍA BELÉN, SARA VIERA, ELENA FERNÁNDEZ-ALVARO, and IÑIGO ANGULO-BARTUREN. "Animal models of efficacy to accelerate drug discovery in malaria." Parasitology 141, no. 1 (June 21, 2013): 93–103. http://dx.doi.org/10.1017/s0031182013000991.

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SUMMARYThe emergence of resistance to artemisinins and the renewed efforts to eradicate malaria demand the urgent development of new drugs. In this endeavour, the evaluation of efficacy in animal models is often a go/no go decision assay in drug discovery. This important role relies on the capability of animal models to assess the disposition, toxicology and efficacy of drugs in a single test. Although the relative merits of each efficacy model of malaria as human surrogate have been extensively discussed, there are no critical analyses on the use of such models in current drug discovery. In this article, we intend to analyse how efficacy models are used to discover new antimalarial drugs. Our analysis indicates that testing drug efficacy is often the last assay in each discovery stage and the experimental designs utilized are not optimized to expedite decision-making and inform clinical development. In light of this analysis, we propose new ways to accelerate drug discovery using efficacy models.

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P.L.Sujatha, K.Anbu Kumar, P.Devendran, S.P.Preetha, and Manikkavasagan Ilangopathy3. "APPLICATION OF COMPUTATIONAL METHODS IN DRUG DISCOVERY." Indian Journal of Veterinary and Animal Sciences Research 53, no. 5 (January 24, 2025): 1–8. https://doi.org/10.56093/ijvasr.v53i5.161975.

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Rational drug design, is the inventive process of finding new medications based on knowledge of the biological target. Drug design involves the design of small molecules that are complementary in shape and charge to the bimolecular target to which they interact and therefore will bind to it. In the experiment based approach, drugs are discovered through trial and error. With high R&D cost and consumption, computational drug discovery helps scientists gain insight into drug receptor interactions and reduce time and cost. Scientists can predict whether the molecule will succeed or fail in the market. Currently, the process of drug designing increasingly relies on computer modeling techniques. This type of modeling is often referred to as computer-aided drug design. In computational drug discovery, different computational tools, methods, and software are used to simulate drug receptor interactions. Using computational drug discovery helps scientists gain insight into drug receptor interactions with less time and cost.

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25

Gomtsyan, A. "Heterocycles in drugs and drug discovery." Chemistry of Heterocyclic Compounds 48, no. 1 (April 2012): 7–10. http://dx.doi.org/10.1007/s10593-012-0960-z.

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Jayaram, Saravanan, Emdormi Rymbai, Deepa Sugumar, and Divakar Selvaraj. "Drug Repurposing: A Paradigm Shift in Drug Discovery." INTERNATIONAL JOURNAL OF APPLIED PHARMACEUTICAL SCIENCES AND RESEARCH 5, no. 04 (June 30, 2020): 60–68. http://dx.doi.org/10.21477/ijapsr.5.4.2.

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The traditional methods of drug discovery and drug development are a tedious, complex, and costly process. Target identification, target validation; lead identification; and lead optimization are a lengthy and unreliable process that further complicates the discovery of new drugs. A study of more than 15 years reports that the success rate in the discovery of new drugs in the fields of ophthalmology, cardiovascular, infectious disease, and oncology to be 32.6%, 25.5%, 25.2% and 3.4%, respectively. A tedious and costly process coupled with a very low success rate makes the traditional drug discovery a less attractive option. Therefore, an alternative to traditional drug discovery is drug repurposing, a process in which already existing drugs are repurposed for conditions other than which were originally intended. Typical examples of repurposed drugs are thalidomide, sildenafil, memantine, mirtazapine, mifepristone, etc. In recent times, several databases have been developed to hasten drug repurposing based on the side effect profile, the similarity of chemical structure, and target site. This work reviews the pivotal role of drug repurposing in drug discovery and the databases currently available for drug repurposing.

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Meert, Theo F. "DRUG DISCRIMINATION IN DRUG DISCOVERY." Behavioural Pharmacology 10, SUPPLEMENT 1 (August 1999): S61. http://dx.doi.org/10.1097/00008877-199908001-00155.

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28

CASHMAN, J. "Drug discovery and drug metabolism." Drug Discovery Today 1, no. 5 (May 1996): 209–16. http://dx.doi.org/10.1016/1359-6446(96)10017-9.

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Varun, Ahuja. "Artificial Intelligence (AI) in Drug Discovery and Medicine." Journal of Clinical Cases & Reports 2, no. 3 (July 30, 2019): 76–80. http://dx.doi.org/10.46619/joccr.2019.2-1043.

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Artificial intelligence (AI) is a branch of computer science that deals with the development of algorithms that seek to simulate human intelligence. The phrase “artificial intelligence” was likely coined during a conference at Dartmouth College in 1956. The earliest work of medical AI dates back to the early 1970s. Over years, AI has found implications in various fields. In this article, we summarize its applications in drug discovery and medicine.

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Zhang, Y. "Open-access and Structured Data in Drug Discovery." Biomedical Data Journal 01, no. 1 (January 2015): 39–41. http://dx.doi.org/10.11610/bmdj.01107.

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Shah, Bhavinkumar. "Revolutionizing Drug Discovery: The Role of Artificial Intelligence." International Journal of Science and Research (IJSR) 12, no. 12 (December 5, 2023): 1948–52. http://dx.doi.org/10.21275/sr231219092956.

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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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Juliet Chiamaka Muoegbunam, Valerie Ezinne Nwankwo, Amarachukwu Ukamaka Onwuzuligbo, Blessing Ogechukwu Umeokoli, Chika Christiana Abba, Festus Basden C. Okoye, and Kenneth Gerald Ngwoke. "A review of nature’s pharmacy: Unveiling the sources, classes and therapeutic potentials of natural products in drug discovery." GSC Biological and Pharmaceutical Sciences 30, no. 3 (March 30, 2025): 053–63. https://doi.org/10.30574/gscbps.2025.30.3.0075.

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Natural products have been an important source of inspiration for the discovery of new drugs, with many successful drugs derived from natural sources. This review aims at providing an overview of the current state of natural products in drug discovery, highlighting their sources, classes, importance and therapeutic potentials. The findings of this review underscore the significant contribution of natural products in drug discovery with an ongoing stream of new compounds being discovered and developed into effective medicines. Furthermore, this review emphasizes the importance of natural products derived from a wide range of sources, including plants, animals, microorganisms and marine organisms. The review concludes with a focus on plants in drug discovery, tracing the historical significance of medicinal plants and highlighting the various bioactive metabolites obtained from plants with potential therapeutic applications.

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Lee, Jonathan A., and Ellen L. Berg. "Neoclassic Drug Discovery." Journal of Biomolecular Screening 18, no. 10 (September 30, 2013): 1143–55. http://dx.doi.org/10.1177/1087057113506118.

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Innovation and new molecular entity production by the pharmaceutical industry has been below expectations. Surprisingly, more first-in-class small-molecule drugs approved by the U.S. Food and Drug Administration (FDA) between 1999 and 2008 were identified by functional phenotypic lead generation strategies reminiscent of pre-genomics pharmacology than contemporary molecular targeted strategies that encompass the vast majority of lead generation efforts. This observation, in conjunction with the difficulty in validating molecular targets for drug discovery, has diminished the impact of the “genomics revolution” and has led to a growing grassroots movement and now broader trend in pharma to reconsider the use of modern physiology-based or phenotypic drug discovery (PDD) strategies. This “From the Guest Editors” column provides an introduction and overview of the two-part special issues of Journal of Biomolecular Screening on PDD. Terminology and the business case for use of PDD are defined. Key issues such as assay performance, chemical optimization, target identification, and challenges to the organization and implementation of PDD are discussed. Possible solutions for these challenges and a new neoclassic vision for PDD that combines phenotypic and functional approaches with technology innovations resulting from the genomics-driven era of target-based drug discovery (TDD) are also described. Finally, an overview of the manuscripts in this special edition is provided.

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Laddha, C. S., A. V. Shelke, Y. V. Vaidya, A. A. Sheikh, and K. R. Biyani. "A Review on Artificial Intellegence in Drug Discovery & Pharmaceutical Industry." Asian Journal of Pharmaceutical Research and Development 11, no. 3 (June 15, 2023): 45–51. http://dx.doi.org/10.22270/ajprd.v11i3.1252.

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Introduction: The use of artificial intelligence (AI) in drug discovery and the pharma industry has been rapidly expanding in recent years. AI algorithms can analyze vast amounts of data, identify patterns, and make predictions that can accelerate drug discovery and improve patient outcomes. Methods: AI is being used in various stages of the drug discovery process, from target identification and lead optimization to clinical trials and post-market surveillance. Machine learning algorithms, neural networks, and natural language processing are among the AI techniques used in drug discovery. Results: AI-based drug discovery has already shown promising results, with several drugs in clinical trials or approved for use that were discovered using AI. AI is also being used to improve clinical trial design and patient selection, as well as to monitor adverse drug events and optimize drug dosing. Conclusion: AI has the potential to transform the drug discovery and pharma industry, making drug development faster, more efficient, and more effective. However, there are still challenges that need to be addressed, such as the need for high-quality data and the potential for bias in AI algorithms. Overall, the use of AI in drug discovery and the pharma industry is an exciting and rapidly evolving field that has the potential to improve patient outcomes and revolutionize healthcare.

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Handa, S. S. "WHY PHYTOPHARMACEUTICAL DRUG DISCOVERY?" INDIAN DRUGS 57, no. 04 (July 1, 2020): 5–6. http://dx.doi.org/10.53879/id.57.04.p0005.

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Dear Reader, “Phytopharmaceutical drug” includes a purified and standardised fraction with defined minimum four bio-active or phyto-chemical compounds (qualitatively and quantitatively assessed) of an extract of a medicinal plant or its part, for internal or external use of human beings or animals for diagnosis, treatment, mitigation or prevention of any disease or disorder but does not include administration by parenteral route as specified in Rule 122 (eb) of the Drugs & Cosmetics (D&C) Govt. of India”. The data requirements have been specified in the Appendix IB of Schedule Y & GMP manufacturing as per Schedule M (part VI) of D&C Rules. Clinical trials for phytopharmaceutical drugs is to be conducted as per applicable rules and guidelines for a new drug.”

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Khan, Saba, Jaya Agnihotri, Sunanda Patil, and Nikhat Khan. "Drug repurposing: A futuristic approach in drug discovery." Journal of Pharmaceutical and Biological Sciences 11, no. 1 (July 15, 2023): 66–69. http://dx.doi.org/10.18231/j.jpbs.2023.011.

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Drug repurposing (DR), also known as drug repositioning, is a strategy aimed at identifying new therapeutic uses for existing drugs. It offers an effective approach to discovering or developing drug molecules with novel pharmacological or therapeutic indications. In recent years, pharmaceutical companies have increasingly embraced the drug repurposing strategy in their drug discovery and development programs, leading to the identification of new biological targets. This strategy is highly efficient, time-saving, cost-effective, and carries a lower risk of failure compared to traditional drug discovery methods. By maximizing the therapeutic value of existing drugs, drug repurposing increases the likelihood of success. It serves as a valuable alternative to the lengthy, expensive, and resource-intensive process of finding new molecular entities (NMEs) through traditional or de novo drug discovery approaches. Drug repurposing combines activity-based or experimental methods with in silico-based or computational approaches to rationally develop or identify new uses for drug molecules. It leverages the existing safety data of drugs tested in humans and redirects their application based on valid target molecules. This approach holds great promise, particularly in addressing rare, difficult-to-treat diseases, and neglected diseases. By utilizing the wealth of knowledge and resources available, drug repurposing presents an emerging strategy for optimizing the therapeutic potential of existing medicines. It offers a pathway to rapidly identify effective treatments and repurpose approved drugs for new indications, benefiting patients and healthcare systems alike.

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Kathiresan, Revathi Meenal. "Drug Discovery using Quantum Simulation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (December 23, 2023): 1–13. http://dx.doi.org/10.55041/ijsrem27768.

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Health care Analysis spread wider throughout the globe enables proper structured resolutions for preventing the avalanche of health-related issues. The foremost goal lies in ensuring the proper safety and security of the patients. Talking about Patient safety, early diagnosis is the predominant key in finding any deadly diseases at an earlier stage, reducing the treatment costs, and increasing the overall survival rates. Also, an important factor that comes in parallel along with patient safety is ‘Drug Discovery’. In current day scenario, Pharma industries are striving a lot to improvise clinical development productivity by examining important potential strategies. The main point of optimization lies in the ‘Drug Discovery’ process. Drug Discovery plays a crucial role in clinical programs, as it helps towards diagnosis of illnesses. When a drug of satisfactory evidence is administered to a target it should ensure sufficient safety of the target. One of the foremost risks here is the availability of better drugs for a disease, or in other words appropriate potency should be ensured by the drug over a target. Despite investing very huge amount over a drug, 90% of it gets failed in clinical trials. In order to overcome the above-mentioned issue with Drug discovery, many pharma industries are relying on AI technologies to ensure, the drug actively engages the target and produce expected therapeutic effect. Along AI another promising technology which is set to revolutionize a wide range of health industry is Quantum Computing. As pharma industries are in urge to provide quality efficient and easily accessible drugs within reach, Quantum computing with built in Quantum mechanics perfectly fits in to derive proper compositions of drugs by trying out various drug-protein-gene interactions and in forecasting drug’s development using quantum algorithms and simulation techniques.

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39

KUWASHIMA, Kenichi. "Drug Discovery Process." Annals of Business Administrative Science 15, no. 3 (2016): 129–38. http://dx.doi.org/10.7880/abas.0160224a.

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Thakur, R. S. "DRUG DISCOVERY COMPLEXITIES." Rajiv Gandhi University of Health Sciences Journal of Pharmaceutical Sciences 4, no. 1 (May 23, 2014): 1–2. http://dx.doi.org/10.5530/rjps.2014.1.1.

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41

Wossnig, Leonard. "Intelligent drug discovery." Physics World 34, no. 5 (July 1, 2021): 39–40. http://dx.doi.org/10.1088/2058-7058/34/05/38.

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Schwardt, Oliver, Brian Cutting, Hartmuth Kolb, and Beat Ernst. "Drug Discovery Today." Frontiers in Medicinal Chemistry - Online 2, no. 1 (January 1, 2005): 533–43. http://dx.doi.org/10.2174/1567204052931050.

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Schwardt, Oliver, Hartmuth Kolb, and Beat Ernst. "Drug Discovery Today." Current Topics in Medicinal Chemistry 3, no. 1 (January 1, 2003): 1–9. http://dx.doi.org/10.2174/1568026033392642.

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Okajima, Nobuyuki. "Combinatorial Drug Discovery." Journal of Pesticide Science 28, no. 1 (2003): 86–93. http://dx.doi.org/10.1584/jpestics.28.86.

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Zenie, Francis H. "Accelerating Drug Discovery." Nature Biotechnology 12, no. 7 (July 1994): 736. http://dx.doi.org/10.1038/nbt0794-736.

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Fischer, János. "Successful Drug Discovery." Chemistry International 42, no. 3 (July 1, 2020): 32–35. http://dx.doi.org/10.1515/ci-2020-0324.

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Wohlleben, Wolfgang, Yvonne Mast, Evi Stegmann, and Nadine Ziemert. "Antibiotic drug discovery." Microbial Biotechnology 9, no. 5 (July 29, 2016): 541–48. http://dx.doi.org/10.1111/1751-7915.12388.

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Szuromi, P. "Rethinking Drug Discovery." Science 303, no. 5665 (March 19, 2004): 1795. http://dx.doi.org/10.1126/science.303.5665.1795.

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Balachandran, Premalatha, and Rajgopal Govindarajan. "Ayurvedic drug discovery." Expert Opinion on Drug Discovery 2, no. 12 (November 26, 2007): 1631–52. http://dx.doi.org/10.1517/17460441.2.12.1631.

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Abrahams, Katherine A., and Gurdyal S. Besra. "Mycobacterial drug discovery." RSC Medicinal Chemistry 11, no. 12 (2020): 1354–65. http://dx.doi.org/10.1039/d0md00261e.

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