Relevant bibliographies by topics / Computer-Aided Drug Discovery

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

Academic literature on the topic 'Computer-Aided Drug Discovery'

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

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Journal articles on the topic "Computer-Aided Drug Discovery"

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

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Synopsis:The role of computers in drug discovery depends on just how much is known about the target macromolecule. If atomic detail of the receptor is known, binding free energy differences between drug variants may be computed. Major effort is being expended in extending the area of applicability of such studies by predicting protein structure based on homologies with known protein crystal data. Where no target structure is available, computational methods can provide leads by defining transition state structures and then using the approach of molecular similarity to define stable mimics to a
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Bajorath, Jürgen. "Computer-aided drug discovery." F1000Research 4 (August 26, 2015): 630. http://dx.doi.org/10.12688/f1000research.6653.1.

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Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approach
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Barrawaz, Aateka Y. "COMPUTER AIDED DRUG DESIGN: A MINI-REVIEW." Journal of Medical Pharmaceutical And Allied Sciences 9, no. 5 (2020): 2584–91. http://dx.doi.org/10.22270/jmpas.v9i5.971.

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New drug discovery and development process is considered much complex process which is time consuming and resources accommodating too. So computer aided drug design are being broadly used to enhance the effectiveness of the drug discovery and development process which ultimately saves time and resources. Various approaches to Computer aided drug design are evaluated to shows potential techniques in accordance with their needs. Two approaches are considered to designing of drug first one is structure-based and second one is Ligand based drug designs. In this review, we are discussing about high
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KIRBOĞA, Kevser Kübra, and Ecir KÜÇÜKSİLLE. "Bilgisayar Destekli İlaç Keşfi Üzerine Bakışlar." Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi 11, no. 2 (2022): 1. http://dx.doi.org/10.55007/dufed.1103457.

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The drug development and discovery process are challenging, take 15 to 20 years, and require approximately 1.5-2 billion dollars, from the critical selection of the target molecule to post-clinical market application. Several computational drug design methods identify and optimize target biologically lead compounds. Given the complexity and cost of the drug discovery process in recent years, computer-assisted drug discovery (CADD) has spread over a broad spectrum. CADD methods support the discovery of target molecules, optimization of small target molecules, analysis, and development processes
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Sharma, Anu, Lalubhai Jangid, Nusrat Shaikh, and Jitendra Bhangale. "Computer-Aided Drug Design Boon in Drug Discovery." Asian Journal of Organic & Medicinal Chemistry 7, no. 1 (2022): 55–64. http://dx.doi.org/10.14233/ajomc.2022.ajomc-p361.

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An innovative sequential step of detecting new medicines or drugs dependent on the information of a target is called drug design. The drug is a small molecule that alters the capacity of a bimolecular, example, protein, receptor or catalyst that leads to restorative incentive for patients. Designing of drug by computational method helped steady use of computational science to find, improve and study drugs as well as biologically related active molecules. The displaying examines like the structure-based plan; ligand-based drugs structure; database looking and restricting partiality dependent on
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Mihai, Dragos Paul, and George Mihai Nitulescu. "Computer-Aided Drug Design and Drug Discovery." Pharmaceuticals 18, no. 3 (2025): 436. https://doi.org/10.3390/ph18030436.

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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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Cohen, Fred E., and Walter Moos. "Computer-aided drug discovery and design." Perspectives in Drug Discovery and Design 1, no. 2 (1993): 251. http://dx.doi.org/10.1007/bf02174526.

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

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Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to v
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Ahirwar, Hemant, Gabbar Kurmi, Rubeena Khan, et al. "Review on QSAR using Anticancer Drug." Asian Journal of Dental and Health Sciences 2, no. 4 (2022): 59–63. http://dx.doi.org/10.22270/ajdhs.v2i4.27.

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

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Ibrahim, Mahmoud Arafat Abd el-hamid. "Developments and applications in computer-aided drug discovery." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/developments-and-applications-in-computeraided-drug-discovery(eb57dde8-6190-4ea6-8fa8-219693788daf).html.

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Noncovalent interactions are of great importance in studies on crystal design and drug discovery. One such noncovalent interaction, halogen bonding, is present between a covalently bound halogen atom and a Lewis base. A halogen bond is a directional interaction caused by the anisotropic distribution of charge on a halogen atom X covalently bound to A, which in turn forms a positive region called σ-hole on the A–X axis. Utilization of halogen bonds in lead optimization have been rarely considered in drug discovery until recently and yet more than 50% of the drug candidates are halogenated. To d
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Sliwoski, Gregory. "Computer Aided Drug Discovery Descriptor Improvement and Application to Obesity-related Therapeutics." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-201000.

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When applied to drug discovery, modern computational systems can provide insight into the highly complex systems underlying drug activity and predict compounds or targets of interest. Many tools have been developed for computer aided drug discovery (CADD), focusing on small molecule ligands, protein targets, or both. The aim of this thesis is the improvement of CADD tools for describing small molecule properties and application of CADD to several stages of drug discovery regarding two targets for the treatment of obesity and related diseases: the neuropeptide Y4 receptor (Y4R) and the melanoco
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Lundborg, Magnus. "Computer-Assisted Carbohydrate Structural Studies and Drug Discovery." Doctoral thesis, Stockholms universitet, Institutionen för organisk kemi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-56411.

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Carbohydrates are abundant in nature and have functions ranging from energy storage to acting as structural components. Analysis of carbohydrate structures is important and can be used for, for instance, clinical diagnosis of diseases as well as in bacterial studies. The complexity of glycans makes it difficult to determine their structures. NMR spectroscopy is an advanced method that can be used to examine carbohydrates at the atomic level, but full assignments of the signals require much work. Reliable automation of this process would be of great help. Herein studies of Escherichia coli O-an
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Wassef, Abram. "Computer aided drug discovery: Design, synthesis and testing of novel anti-cancer agents." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14546.

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The continuous enhancements of computational tools for drug discovery, have greatly improved the pre-clinical stages of pharmaceutical research, to a higher level of efficiency and greater speed. The combination between homology modelling methods in structure-based drug design, and the chemoinformatics techniques in lead optimization, has allowed successful development of small molecule inhibitors with 30 folds higher potency of best known ASCT2 inhibitors. Three dimensional model of ASCT2 protein was constructed via fold recognition technique, the preferred method in absence of highly simil
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Chemi, Giulia. "Computer-aided drug discovery methodologies for the identification and optimization of bioactive compounds." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1095491.

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In silico methodologies have become an essential part of the modern drug discovery process. This thesis covers a wide range of computational approaches, and all the techniques employed can be grouped and divided in two big different classes: Ligand-Based methods and Structure-Based methods. Particular attention was payed to the evaluation of drug-like features of the identified molecules during both Structure-Based and Ligand-Based projects, in order to propose hit compounds with satisfactory pharmacokinetic profiles. This thesis work is divided in two main chapters according to the two met
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Athri, Prashanth. "Application of Computer-Aided Drug Discovery Methodologies Towards the Rational Design of Drugs Against Infectious Diseases." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/chemistry_diss/20.

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Computer-aided drug discovery involves the application of computer science and programming to solve chemical and biological problems. Specifically, the QSAR (Quantitative Structure Activity Relationships) methodology is used in drug development to provide a rational basis of drug synthesis, rather than a trial and error approach. Molecular dynamics (MD) studies focus on investigating the details of drug-target interactions to elucidate various biophysical characteristics of interest. Infectious diseases like Trypanosoma brucei rhodesiense (TBR) and P. falciparum (malaria) are responsible for m
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Aprahamian, Melanie Lorraine. "Studies in Computational Biochemistry: Applications to Computer Aided Drug Discovery and Protein Tertiary Structure Prediction." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554977217363556.

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Kim, Stephanie S. "Structure-based Computer-Aided Drug Discovery: Applications for Polypharmacology and Characterizing Non-globular Regions of Proteins." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586984837053744.

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Fienberg, Stephen. "Development of N-domain selective Angiotensin-I Converting Enzyme (ACE) inhibitors using Computer Aided Drug Discovery (CADD)." Doctoral thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/25656.

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Angiotensin-I (Ang-I) converting enzyme (ACE) is a zinc metalloprotease that plays a vital role in the Renin Angiotensin Aldosterone System (RAAS) and is a key antihypertensive drug target. In addition to Ang-I, ACE cleaves many other physiological substrates, thus extending its function beyond the regulation of blood pressure. Somatic ACE (sACE) consists of two structurally homologous yet distinct catalytic sites termed the N- and C-domains. The two catalytic domains of ACE have distinct substrate affinities and play different regulatory roles. The antifibrotic tetrapeptide Ac-SDKP is hydroly
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Smith, Emmanuel William. "Computer-Aided Structure-Based Drug Discovery: CXCL12, P. aeruginosa LpxA, and the Tiam1 PDZ Domain." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5614.

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For structure-based drug discovery, structural information of a target protein is necessary. NMR, or X-ray crystallography can provide necessary information on active site configuration that can lead a successful virtual screening campaign into identifying binders that may then be optimized into potent inhibitors. However, many challenges exist in the structure-based drug discovery cycle. For instance, structure determination of a protein of interest can many times be a daunting task. In addition, complex structure determination, which can allow essential characterization of protein-ligand int
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Books on the topic "Computer-Aided Drug Discovery"

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Zhang, Wei, ed. Computer-Aided Drug Discovery. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3521-5.

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S, Rapaka Rao, Hawks Richard L, and National Institute on Drug Abuse., eds. Medications development: Drug discovery, databases, and computer-aided drug design. U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, National Institute on Drug Abuse, 1993.

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Singh, Sanjeev Kumar, ed. Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8936-2.

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Alexandre, Varnek, Tropsha Alex, and Royal Society of Chemistry (Great Britain)., eds. Chemoinformatics approaches to virtual screening. RSC Pub., 2008.

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Zhang, Wei. Computer-Aided Drug Discovery. Springer New York, 2018.

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Zhang, Wei. Computer-Aided Drug Discovery. Humana Press, 2016.

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Gaudencio, Susana P., and Florbela Pereira, eds. Marine Drug Discovery through Computer-Aided Approaches. MDPI, 2024. http://dx.doi.org/10.3390/books978-3-0365-9912-0.

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(Editor), Rao S. Rapaka, and Richard L. Hawks (Editor), eds. Medications Development: Drug Discovery, Databases, And Computer-aided Drug Design. Diane Pub Co, 2004.

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Ye, Fei, Zhongjie Liang, and Ylvain Broussy, eds. Computer-Aided Drug Design: Drug Discovery, Computational Modelling, and Artificial Intelligence. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88976-784-7.

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Singh, Sanjeev Kumar. Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design. Springer Singapore Pte. Limited, 2021.

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Book chapters on the topic "Computer-Aided Drug Discovery"

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Lu, Pinyi, David R. Bevan, Andrew Leber, Raquel Hontecillas, Nuria Tubau-Juni, and Josep Bassaganya-Riera. "Computer-Aided Drug Discovery." In Accelerated Path to Cures. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73238-1_2.

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Singh, Birbal, Gorakh Mal, Sanjeev K. Gautam, and Manishi Mukesh. "Computer-Aided Drug Discovery." In Advances in Animal Biotechnology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21309-1_44.

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Bharatam, Prasad V. "Computer-Aided Drug Design." In Drug Discovery and Development. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5534-3_6.

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Kumar, Anil, and Praffulla Kumar Arya. "Database Resources for Drug Discovery." In Computer-Aided Drug Design. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6815-2_5.

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Zhang, Shuxing. "Computer-Aided Drug Discovery and Development." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-012-6_2.

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Pathak, Rajesh Kumar, Dev Bukhsh Singh, Mamta Sagar, Mamta Baunthiyal, and Anil Kumar. "Computational Approaches in Drug Discovery and Design." In Computer-Aided Drug Design. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6815-2_1.

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Khanna, Varun, and Shoba Ranganathan. "Chemogenomics Approach to Computer Aided Drug Discovery." In Post-genomic Approaches in Drug and Vaccine Development. River Publishers, 2022. http://dx.doi.org/10.1201/9781003339090-5.

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Cronin, Mark T. D. "Computer-aided prediction of drug toxicity and metabolism." In Modern Methods of Drug Discovery. Birkhäuser Basel, 2003. http://dx.doi.org/10.1007/978-3-0348-7997-2_13.

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Pedreira, Júlia Galvez Bulhões, and Pedro de Sena Murteira Pinheiro. "Kinase Inhibitors and Computer-Aided Drug Design Methods." In Computer-Aided Drug Discovery and Design. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-76718-0_18.

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Rognan, Didier. "Fragment-Based Approaches and Computer-Aided Drug Discovery." In Topics in Current Chemistry. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/128_2011_182.

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Conference papers on the topic "Computer-Aided Drug Discovery"

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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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Hastedt, Friedrich, Klaus Hellgardt, Sophia Yaliraki, Antonio del Rio Chanona, and Dongda Zhang. "Computational Assessment of Molecular Synthetic Accessibility using Economic Indicators." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.175859.

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The rapid advancement of computational drug discovery has enabled the generation of vast virtual libraries of promising drug candidates. However, evaluating the synthetic accessibility (SA) of these compounds remains a critical bottleneck. While computer-aided synthesis planning (CASP) tools can provide synthesis routes to the candidate, their computational demands make them impractical for large-scale screening. Existing rapid SA scoring methods, struggle to generalize to out-of-distribution molecules and do not account for economic viability. To address these challenges, we present MolPrice,
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Bajorath, J., T. E. Klein, T. P. Lybrand, and J. Novotny. "COMPUTER-AIDED DRUG DISCOVERY: FROM TARGET PROTEINS TO DRUG CANDIDATES." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789814447300_0040.

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Wang, Jing-Fang, Lin Li, Dong-Qing Wei, and Kuo-Chen Chou. "Discovery of Anti-Hiv Drugs Using Computer Aided Drug Designing Tools." In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icbbe.2007.87.

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Aswani, Anil, and Claire Tomlin. "Computer-aided drug discovery for pathway and genetic diseases." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5717302.

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Manu, Daniel, Yi Sheng, Junhuan Yang, et al. "FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper." In 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD). IEEE, 2021. http://dx.doi.org/10.1109/iccad51958.2021.9643440.

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Munuganti, Ravi, Daksh Thaper, Adeleke Aguda, Soojin Kim, Olena Sivak, and Amina Zoubeidi. "Abstract 14: Computer-aided discovery of small-molecule inhibitors targeting neural transcription factor BRN2 in neuroendocrine prostate tumors." In Abstracts: AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; January 9-12, 2020; San Diego, CA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1557-3265.advprecmed20-14.

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Zheng, Yan, Song Wu, Junyu Lin, et al. "Cross-View Contrastive Fusion for Enhanced Molecular Property Prediction." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/621.

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Machine learning based molecular property prediction has been a hot topic in the field of computer aided drug discovery (CADD). However, current MPP methods face two prominent challenges: 1) single-view MPP methods do not sufficiently exploit the complementary information of molecular data across multiple views, generally producing suboptimal performance, and 2) most existing multi-view MPP methods ignore the disparities in data quality among different views, inadvertently introducing the risk of models being overshadowed by inferior views. To address the above challenges, we introduce a novel
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Gu, Chunyu, Kiran V. Mahasenan, Tyler Auvil, et al. "Abstract 3298: Structure-based computer-aided drug design to discover novel small molecules that target brain tumor stem cells." In Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1538-7445.am2011-3298.

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Reports on the topic "Computer-Aided Drug Discovery"

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Opportunities for Russian Nuclear Weapons Institute developing computer-aided design programs for pharmaceutical drug discovery. Final report. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/505318.

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