Relevant bibliographies by topics / Drug discovery

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Drug discovery'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Drug discovery"

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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 (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, Guangd
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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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Jadhav, Mr Gahininath Thansing, and Mr Rahul Bhavlal Jadhav. "Drug Discovery and Development Process." International Journal of Research Publication and Reviews 5, no. 1 (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 (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 (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 (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 wh
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Alehaideb, Zeyad, Nimer Mehyar, Mai Al Ajaji, et al. "KAIMRC’S Second Therapeutics Discovery Conference." Proceedings 43, no. 1 (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 SudairPh
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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 (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 realiz
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Antonelli, Alessandro. "Drug Discovery." Current Pharmaceutical Design 28, no. 3 (2022): 179. http://dx.doi.org/10.2174/1381612828666220103172626.

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

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Dissertations / Theses on the topic "Drug discovery"

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Wuitschik, Georg. "Oxetanes in drug discovery /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17929.

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Herzberg, Benjamin. "Fluorous Drug-Affinity Proteomics for Cancer Drug Discovery." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:15821582.

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Identifying the intracellular targets of small molecules – target ID – is a major problem in chemical biology with broad application to the discovery and development of novel therapies. Traditional target ID studies have relied on drug-affinity chromatography to separate biological mixtures combined with mass spectrometry shotgun sequencing for peptide identification. This workflow is limited, however, by low specificity for unique peptides, high demand for cellular material, unknown depth of profiling, and other problems. To address these problems, we explore and describe here a novel strateg
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Williams, Kevin. "Active Learning for drug discovery." Thesis, Aberystwyth University, 2014. http://hdl.handle.net/2160/eaf6e66e-17fe-41a9-ac1d-9939abbb8331.

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This thesis describes work conducted to enable Robot Scientist Eve to autonomously evaluate drug-like chemicals during high throughput experiments. Eve tests libraries of chemical compounds against yeast-based targets expressing parasite and host (human) proteins (i.e. DHFR, NMT & PGK); the parasites included in this study are responsible for an array of neglected tropical diseases. The raw data for yeast growth curves from an initial screen were evaluated, and decision tree rules were constructed to describe the relative activity and toxicity of compounds. These rules were verified, and versi
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Bhalla, Nikhil. "Biosensors for drug discovery applications." Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.683538.

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This research developed a biosensor for kinase drug discovery applications. In particular it combined electronic techniques with optical techniques to understand the phosphorylation of proteins. There are two major electronic characteristics of phosphorylation that aid in its detection and subsequently biosensor development: first is the release of a proton upon phosphorylation of a protein (change in pH) and second is the addition of negative charge to the protein upon its phosphorylation. The work in this thesis reports an electrolyte–insulator–semiconductor sensing structures to detect the
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Sriram, Ranganath. "Inventory management for drug discovery." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43863.

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Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; and, (M.B.A.) -- Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2008.<br>Includes bibliographical references (p. 67-69).<br>This thesis documents a study carried out at the Novartis Institutes for BioMedical Research (NIBR) in Cambridge, MA. The study focused on the development of inventory management processes for laboratory consumables. The pharmaceutical R&D process is characterized by a dynamic project portfolio, which res
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Roberts, Rebecca Anne M. B. A. Massachusetts Institute of Technology. "Process optimization in drug discovery." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39693.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2007.<br>Includes bibliographical references (p. 69-70).<br>Novartis is one of the largest pharmaceutical companies in the world, with their research headquarters (Novartis Institutes for BioMedical Research) located in Cambridge MA. In this thesis, I explore Novartis's process for developing drugs, specifically the earlier stages of research leading
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Tan, Eu Vian. "Holographic sensors for drug discovery." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611525.

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Wilson, Ian. "Halogenated heterocycles for drug discovery." Thesis, Durham University, 2011. http://etheses.dur.ac.uk/863/.

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Within a number of industries, and particularly within the pharmaceutical industry, there is a desire for reliable, high yielding routes towards large numbers of valuable small molecules that allow a wide range of products to be synthesised. Heterocyclic compounds are particularly sought after as useful compounds, with an estimated 70% of pharmaceutical products being based on heterocyclic structures. A drawback of many traditional routes towards heterocyclic compounds is that the range of substituents that may be placed around the ring is limited. This is especially limiting if ring substitue
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Gage, Zoe O. "Interferon, viruses and drug discovery." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/10127.

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The interferon (IFN) response is a crucial component of cellular innate immunity, vital for controlling virus infections. Dysregulation of the IFN response however can lead to serious medical conditions including autoimmune disorders. Modulators of IFN induction and signalling could be used to treat these diseases and as tools to further understand the IFN response and viral infections. We have developed cell-based assays to identify modulators of IFN induction and signalling, based on A549 cell lines where a GFP gene is under the control of the IFN-β promoter (A549/pr(IFN-β).GFP) and the ISRE
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Maniyar, Dharmesh M. "Probabilistic methods for drug discovery." Thesis, Aston University, 2006. http://publications.aston.ac.uk/10615/.

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This thesis introduces a flexible visual data exploration framework which combines advanced projection algorithms from the machine learning domain with visual representation techniques developed in the information visualisation domain to help a user to explore and understand effectively large multi-dimensional datasets. The advantage of such a framework to other techniques currently available to the domain experts is that the user is directly involved in the data mining process and advanced machine learning algorithms are employed for better projection. A hierarchical visualisation model guide
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Books on the topic "Drug discovery"

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Maxwell, Robert A., and Shohreh B. Eckhardt. Drug Discovery. Humana Press, 1990. http://dx.doi.org/10.1007/978-1-4612-0469-5.

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Li, Jie Jack, and E. J. Corey, eds. Drug Discovery. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118354483.

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Sneader, Walter. Drug Discovery. John Wiley & Sons, Ltd., 2005.

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Dikshit, Madhu, ed. Drug Discovery and Drug Development. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8002-4.

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1948-, Gad Shayne C., ed. Drug discovery handbook. Wiley-Interscience, 2005.

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Isherwood, Beverley, and Angelique Augustin, eds. Phenotypic Drug Discovery. Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781839160721.

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Huang, Ziwei, ed. Drug Discovery Research. John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470131862.

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Kim, Kyu-Won, Jae Kyung Roh, Hee-Jun Wee, and Chan Kim. Cancer Drug Discovery. Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-024-0844-7.

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Ward, Richard A., and Frederick W. Goldberg, eds. Kinase Drug Discovery. Royal Society of Chemistry, 2018. http://dx.doi.org/10.1039/9781788013093.

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Woster, Patrick, and Robert Casero, eds. Polyamine Drug Discovery. Royal Society of Chemistry, 2011. http://dx.doi.org/10.1039/9781849733090.

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Book chapters on the topic "Drug discovery"

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Wang, Yuxuan, and Ross D. King. "Extrapolation is Not the Same as Interpolation." In Discovery Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_19.

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AbstractWe propose a new machine learning formulation designed specifically for extrapolation. The textbook way to apply machine learning to drug design is to learn a univariate function that when a drug (structure) is input, the function outputs a real number (the activity): F(drug) → activity. The PubMed server lists around twenty thousand papers doing this. However, experience in real-world drug design suggests that this formulation of the drug design problem is not quite correct. Specifically, what one is really interested in is extrapolation: predicting the activity of new drugs with high
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Saunders, J. "Drug discovery." In Principles of Molecular Recognition. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2168-2_6.

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Turner, J. Rick. "Drug Discovery." In New Drug Development. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6418-2_3.

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Yin, Min-Jean. "Drug Discovery." In Encyclopedia of Cancer. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-27841-9_7080-4.

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Batista-Navarro, Riza Theresa. "Drug Discovery." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1340.

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Yin, Min-Jean. "Drug Discovery." In Encyclopedia of Cancer. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-46875-3_7080.

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Ramakrishnan, Geetha. "Drug Discovery." In Translational Bioinformatics and Its Application. Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-1045-7_1.

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Ramsden, Jeremy. "Drug Discovery." In Computational Biology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-45607-8_27.

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Chandra, Nagasuma. "Drug Discovery." In Systems Biology of Tuberculosis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4966-9_9.

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Mahato, Ram I., Ajit S. Narang, and Virender Kumar. "Drug Discovery." In Pharmaceutical Dosage Forms and Drug Delivery, 4th ed. CRC Press, 2024. http://dx.doi.org/10.1201/9781003389378-2.

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Conference papers on the topic "Drug discovery"

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Hande, Akash, and Nikhita Chambhare. "Drug Discovery and Development Process." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842909.

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Navarro, Álvaro Serrano, Juan Ignacio Alvarez Arenas, Alberto Gonzalez Calatayud, Jose Antonio Enríquez, Fernando Martínez De Benito, and Fátima Sanchez Cabo. "Virtual-Cardio-Drug: Ai-Powered Sbvs for Cardiovascular Drug Discovery." In 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2025. https://doi.org/10.1109/cbms65348.2025.00118.

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Quraishi, Aadam, Ali Hamid AbdulHussein, Maki Mahdi Abdulhasan, Ahmed Shihab Ahmed, Adnan Allawi Ftaiet, and Bhavani Prasad Kasaraneni. "Computer Aided Drug Design for Enhanced Drug Discovery and Development Processes." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070716.

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Mahameed, Ans Ibrahim, Fatima Ayad Abd Al Zahraa, Ahmed Bahaaulddin A.Alwahhab, et al. "Machine Learning for Drug Discovery and Development." In 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA). IEEE, 2025. https://doi.org/10.1109/ichora65333.2025.11017119.

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Saxena, Kushagra, Ankita Srivastava, and Hemant Kumar. "Advancements in Drug Discovery Through Machine and Deep Learning Concepts in Early-Stage of Drug Discovery for Apoptosis." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986606.

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Nayyar, Akansha, Rahul Shrivastava, and Shruti Jain. "Drug Discovery for Mycobacterium Tuberculosis: An Experimental Analysis." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10751822.

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Srivastava, Atul, Ayush Kumar Srivastava, Anuradha Pillai, Kamlesh Kumar Singh, and Vijay Shankar Sharma. "Predictive Modelling of Drug Discovery Using Machine Learning." In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2024. https://doi.org/10.1109/upcon62832.2024.10983700.

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Cole, Mary. "Illuminating Drug Discovery." In Biomedical Topical Meeting. OSA, 2006. http://dx.doi.org/10.1364/bio.2006.tub2.

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Hu, Zengjian. "Drug Discovery in the Post-Genomic Era: Systems-Based Drug Discovery." In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icbbe.2007.107.

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Biswas, Risab, Avirup Basu, Abhishek Nandy, Arkaprova Deb, Kazi Haque, and Debashree Chanda. "Drug Discovery and Drug Identification using AI." In 2020 Indo-Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN). IEEE, 2020. http://dx.doi.org/10.1109/indo-taiwanican48429.2020.9181309.

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Reports on the topic "Drug discovery"

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Anderson, Burt, Richard Heller, Ed Turos, and Mark Mclaughlin. Drug Discovery, Design and Delivery. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada563482.

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Dr. Edward L. D'Antonio, Dr Edward L. D'Antonio. Early-Stage Drug Discovery for Chagas' Disease. Experiment, 2018. http://dx.doi.org/10.18258/11365.

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Lightstone, F., and B. Bennion. Computational Biology for Drug Discovery and Characterization. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/948962.

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Brueggemeier, Robert W. Drug Discovery and Structural Bioinformatics in Breast Cancer. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada384146.

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Blacklow, Stephen C. Drug Discovery for Breast Cancer by Mirror-Image Display. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada418720.

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Foroozesh, Maryam. A Drug Discovery Partnership for Personalized Breast Cancer Therapy. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada568389.

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Foroozesh, Maryam, Thomas Weise, Jayalakshmi Sridhar, et al. A Drug Discovery Partnership for Personalized Breast Cancer Therapy. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada590425.

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Tesfaselassie, Elias. Antimalarial Drug Discovery using Triazoles to Overcome Chloroquine Resistance. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2503.

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Bellows, David S. Anti-Cancer Drug Discovery Using Synthetic Lethal Chemogenetic (SLC) Analysis. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada434327.

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Wirth, Dyann F. New Strategies for Drug Discovery and Development for Plasmodium Falciparum. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada375802.

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