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: 31 July 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Drug discovery.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Drug discovery"

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Drug discovery"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
Abstract:
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 strategy for cell lysis and drug-affinity that we call “fluorous proteomics.” By conjugating a small molecule to a perfluorinated alkane, we hypothesized that we could achieve superior recovery, specificity, and identification, allowing us to identify previously unknown drug targets with drug-affinity methods. We establish the conditions for fluorous proteomics and synthesize fluorinated probes for two drugs as a proof-of-concept. Lenalidomide, a derivative of thalidomide with unknown intracellular targets but widespread clinical use, is investigated and novel binders are identified. A particular derivative, 5HPP33, is singled out for potential future drug development. JQ1, an inhibitor of BET bromodomains in development as a treatment for hematological malignancies, is used to compare biotinylated versus fluorous tags and to identify new binders of possible therapeutic relevance. We conclude that fluorous proteomics retains high potential as an alternative to traditional drug-affinity chromatography strategies and may aid in target ID going forward, but is not without complications.
APA, Harvard, Vancouver, ISO, and other styles
3

Williams, Kevin. "Active Learning for drug discovery." Thesis, Aberystwyth University, 2014. http://hdl.handle.net/2160/eaf6e66e-17fe-41a9-ac1d-9939abbb8331.

Full text
Abstract:
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 versions were subsequently developed for application to routine mass and confirmation screens. Consequently, many potential lead drug-like candidates have been identified in the Maybridge Hitfinder library; several compounds from an approved drug library (the Johns Hopkins Clinical Compound Library) have also been confirmed as exhibiting activity against these yeast-based targets. Further in vivo study of some JHCCL compounds is in progress using extracted parasite proteins; preliminary results indicate the potential for repositioning Triclosan and Tnp-470 as having anti-malarial behaviour based on their interaction with Plasmodium sp. DHFR proteins. In the second phase of the programme, a prototype Active Learning strategy was applied (active k-optimisation) to partial mass screen data as a seed; this allowed Eve to select compounds by assessing and predicting quantitative structure activity relationships (QSAR) between seed and unknown compounds. Simulations of learning and testing QSAR cycles showed that Eve would be able to select active compounds more efficiently under such a regime. Other strategies have been developed that further improve selection efficiency for active compounds, and also promote the ability to find rare category compounds. An econometric model has been developed to demonstrate the potential beneficial impact of Active Learning strategies on the execution costs for such screens.
APA, Harvard, Vancouver, ISO, and other styles
4

Bhalla, Nikhil. "Biosensors for drug discovery applications." Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.683538.

Full text
Abstract:
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 pH changes associated with phosphorylation and metal–insulator–semiconductor structures to detect the charge change upon phosphorylation of proteins. Major application of the developed devices would be to screen inhibitors of kinase that mediate phosphorylation of proteins. Inhibitors of kinase act as drugs to prevent or cure diseases due to the phosphorylation of proteins. With the advancements in VLSI and microfluidics technology this method can be extended into arrays for high throughput screening for discovering drugs.
APA, Harvard, Vancouver, ISO, and other styles
5

Sriram, Ranganath. "Inventory management for drug discovery." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43863.

Full text
Abstract:
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.
Includes bibliographical references (p. 67-69).
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 results in a great diversity of stock-keeping-units, low repeat order rates and high variability in consumption rates. These factors create significant challenges for the design of inventory management processes. We first present an assessment and diagnosis of the current state of inventory management at NIBR, using data gathered from various NIBR sites as well as other companies. We discuss underlying drivers that influence current behavior, and identify opportunities for improvement. We then develop alternative models for inventory management and compare these models along several dimensions such as stock room location & control, inventory ownership and replenishment options. We recommend the use of consolidated department level stock rooms as the most suitable option for NIBR. Detailed implementation plans are then developed and validated through a case study. We present key findings and recommendations for implementation, and discuss opportunities for future projects.
by Ranganath Sriram.
M.B.A.
S.M.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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.
Includes bibliographical references (p. 69-70).
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 to high throughput screening. During the course of a 6.5 month, on-site project, Novartis's processes were identified, data were collected and relevant literature in product development and organizational structure were surveyed. Based on the accumulation of this information, several opportunities for improvement were identified and from these, recommendations were developed and implemented. This thesis considers the improvements Novartis could see in their drug discovery process by improving communication between organizations. In particular, I suggest that the company could benefit in cycle time and quality by designing and following more robust lateral processes and by moving their communication mode closer to integrative problem solving.
(cont.) Following these recommendations, I investigated why Novartis did not already have these processes in place. I hypothesize that the main reason for this is because the research organization at Novartis is focused primarily on exploration, therefore their ability and need to coordinate has not been an area of focus. Novartis has made a very deliberate effort to design an organization that promotes novel drug discovery; perhaps sacrificing cycle time and process efficiency. Because of this strong focus on drug discovery, Novartis has not had opportunity to design and implement efficient processes. By bring in interns from MIT's Leaders for Manufacturing Program, the company is beginning to explore ways to improve their processes without sacrificing their ability to develop novel drugs.
by Rebecca Anne Roberts.
S.M.
M.B.A.
APA, Harvard, Vancouver, ISO, and other styles
7

Tan, Eu Vian. "Holographic sensors for drug discovery." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611525.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
Abstract:
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 substituents have to be placed early in a synthesis, reducing the opportunities for elaboration at a late stage. Our approach is to take highly halogenated heterocyclic systems and use them as scaffolds for the synthesis of novel compounds by the sequential replacement of halogen atoms with other functionalities. This approach has led to the generation of a number of novel highly substituted heterocyclic species.
APA, Harvard, Vancouver, ISO, and other styles
9

Gage, Zoe O. "Interferon, viruses and drug discovery." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/10127.

Full text
Abstract:
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 containing MxA promoter (A549/pr(ISRE).GFP) respectively. The assays were optimized, miniaturized and validated as suitable for HTS by achieving Z' Factor scores >0.6. A diversity screen of 15,667 compounds using the IFN induction reporter assay identified 2 hit compounds (StA-IFN-1 and StA-IFN-4) that were validated as specifically inhibiting IFNβ induction. Characterisation of these molecules demonstrated that StA-IFN-4 potently acts at, or upstream, of IRF3 phosphorylation. We successfully expanded this HTS platform to target viral interferon antagonists acting upon IFN-signalling. An additional assay was developed where the A549/pr(ISRE).GFP.RBV-P reporter cell line constitutively expresses the Rabies virus phosphoprotein. A compound inhibiting viral protein function will restore GFP expression. The assay was successfully optimized for HTS and used in an in-house screen. We further expanded this assay by placing the expression of RBV-P under the control of an inducible promoter. This demonstrates a convenient approach for assay development and potentiates the targeting of a variety of viral IFN antagonists for the identification of compounds with the potential to develop a novel class of antiviral drugs.
APA, Harvard, Vancouver, ISO, and other styles
10

Maniyar, Dharmesh M. "Probabilistic methods for drug discovery." Thesis, Aston University, 2006. http://publications.aston.ac.uk/10615/.

Full text
Abstract:
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 guided by a domain expert allows them to obtain an informed segmentation of the input space. Two other components of this thesis exploit properties of these principled probabilistic projection algorithms to develop a guided mixture of local experts algorithm which provides robust prediction and a model to estimate feature saliency simultaneously with the training of a projection algorithm. Local models are useful since a single global model cannot capture the full variability of a heterogeneous data space such as the chemical space. Probabilistic hierarchical visualisation techniques provide an effective soft segmentation of an input space by a visualisation hierarchy whose leaf nodes represent different regions of the input space. We use this soft segmentation to develop a guided mixture of local experts (GME) algorithm which is appropriate for the heterogeneous datasets found in chemoinformatics problems. Moreover, in this approach the domain experts are more involved in the model development process which is suitable for an intuition and domain knowledge driven task such as drug discovery. We also derive a generative topographic mapping (GTM) based data visualisation approach which estimates feature saliency simultaneously with the training of a visualisation model.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Drug discovery"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

1948-, Gad Shayne C., ed. Drug discovery handbook. Hoboken, N.J: Wiley-Interscience, 2005.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Drug discovery"

1

Wang, Yuxuan, and Ross D. King. "Extrapolation is Not the Same as Interpolation." In Discovery Science, 277–92. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_19.

Full text
Abstract:
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 higher activity than any existing ones. Our new formulation for extrapolation is based around learning a bivariate function that predicts the difference in activities of two drugs: F(drug1, drug2) → signed difference in activity. This formulation is general and potentially suitable for problems to find samples with target values beyond the target value range of the training set. We applied the formulation to work with support vector machines (SVMs), random forests (RFs), and Gradient Boosting Machines (XGBs). We compared the formulation with standard regression on thousands of drug design datasets, and hundreds of gene expression datasets. The test set extrapolation metrics use the concept of classification metrics to count the identification of extraordinary examples (with greater values than the training set), and top-performing examples (within the top 10% of the whole dataset). On these metrics our pairwise formulation vastly outperformed standard regression for SVMs, RFs, and XGBs. We expect this success to extrapolate to other extrapolation problems.
APA, Harvard, Vancouver, ISO, and other styles
2

Saunders, J. "Drug discovery." In Principles of Molecular Recognition, 137–67. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2168-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Turner, J. Rick. "Drug Discovery." In New Drug Development, 21–34. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6418-2_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Yin, Min-Jean. "Drug Discovery." In Encyclopedia of Cancer, 1–3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-27841-9_7080-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Batista-Navarro, Riza Theresa. "Drug Discovery." In Encyclopedia of Systems Biology, 617. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1340.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Yin, Min-Jean. "Drug Discovery." In Encyclopedia of Cancer, 1429–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-46875-3_7080.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ramakrishnan, Geetha. "Drug Discovery." In Translational Bioinformatics and Its Application, 3–28. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-1045-7_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Chandra, Nagasuma. "Drug Discovery." In Systems Biology of Tuberculosis, 179–92. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4966-9_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Ramsden, Jeremy. "Drug Discovery." In Computational Biology, 365–71. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-45607-8_27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Talevi, Alan, and Carolina L. Bellera. "Drug Discovery Paradigms: Phenotypic-Based Drug Discovery." In Drug Target Selection and Validation, 25–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95895-4_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Drug discovery"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Xie, X. "GPCR-targeted drug discovery." In 67th International Congress and Annual Meeting of the Society for Medicinal Plant and Natural Product Research (GA) in cooperation with the French Society of Pharmacognosy AFERP. © Georg Thieme Verlag KG, 2019. http://dx.doi.org/10.1055/s-0039-3399677.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Gubernator, Klaus. "Evolutionary drug discovery (abstract)." In the second annual international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/279069.279101.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Kun, Weiwei Liu, Yong Luo, Xiantao Cai, Jia Wu, and Wenbin Hu. "Zero-shot Learning for Preclinical Drug Screening." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/234.

Full text
Abstract:
Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds, often with unknown drug responses. This presents a challenge, rendering supervised deep learning methods unsuitable for such scenarios. In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening. Specifically, we propose a Multi-branch Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can be seamlessly integrated with conventional DRP methods, learning invariant features from the prior response data of similar drugs to enhance real-time predictions of unlabeled compounds. The results of experiments on two large drug response datasets showed that MSDA efficiently predicts drug responses for novel compounds, leading to a general performance improvement of 5-10% in the preclinical drug screening phase. The significance of this solution resides in its potential to accelerate the drug discovery process, improve drug candidate assessment, and facilitate the success of drug discovery. The code is available at https://github.com/DrugD/MSDA.
APA, Harvard, Vancouver, ISO, and other styles
7

Hecht, David A., Phillip C. Y. Sheu, and Jeffrey J. P. Tsai. "SCDL Applications to Drug Discovery." In 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2009. http://dx.doi.org/10.1109/bibe.2009.82.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Cunningham, Brian T. "Laser biosensors for drug discovery." In 2014 72nd Annual Device Research Conference (DRC). IEEE, 2014. http://dx.doi.org/10.1109/drc.2014.6872273.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kunkel, Eric J. "Systems biology in drug discovery." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259390.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kanigan, Tanya S., Colin J. H. Brenan, Serge Lafontaine, Luke Sosnowski, Peter G. Madden, and Ian W. Hunter. "Living chips for drug discovery." In BiOS 2000 The International Symposium on Biomedical Optics, edited by Patrick A. Limbach, John C. Owicki, Ramesh Raghavachari, and Weihong Tan. SPIE, 2000. http://dx.doi.org/10.1117/12.380509.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Drug discovery"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Foroozesh, Maryam, Thomas Weise, Jayalakshmi Sridhar, Barbara Beckman, Matthew Burow, Frank Jones, and Cheryl Stevens. A Drug Discovery Partnership for Personalized Breast Cancer Therapy. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada590425.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Drug discovery' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography