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Journal articles on the topic 'In silico design'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 June 2025

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1

DeWitt, Natalie. "In silico vaccine design?" Nature Biotechnology 17, no. 6 (1999): 523. http://dx.doi.org/10.1038/9813.

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2

Kotz, Joanne. "In silico drug design." Science-Business eXchange 6, no. 3 (2013): 50. http://dx.doi.org/10.1038/scibx.2013.50.

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3

Zhao, Jun, Ruth Nussinov, Wen-Jin Wu, and Buyong Ma. "In Silico Methods in Antibody Design." Antibodies 7, no. 3 (2018): 22. http://dx.doi.org/10.3390/antib7030022.

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4

Dahiyat, Bassil I. "In silico design for protein stabilization." Current Opinion in Biotechnology 10, no. 4 (1999): 387–90. http://dx.doi.org/10.1016/s0958-1669(99)80070-6.

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5

Konteatis, Zenon D. "In silico fragment-based drug design." Expert Opinion on Drug Discovery 5, no. 11 (2010): 1047–65. http://dx.doi.org/10.1517/17460441.2010.523697.

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6

Boetker, Johan, Dhara Raijada, Johanna Aho, et al. "In silico product design of pharmaceuticals." Asian Journal of Pharmaceutical Sciences 11, no. 4 (2016): 492–99. http://dx.doi.org/10.1016/j.ajps.2016.02.010.

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7

Baginski, Maciej, and Katarzyna Serbakowska. "In silico design of telomerase inhibitors." Drug Discovery Today 25, no. 7 (2020): 1213–22. http://dx.doi.org/10.1016/j.drudis.2020.04.024.

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8

Foscato, Marco, and Vidar R. Jensen. "Automated in Silico Design of Homogeneous Catalysts." ACS Catalysis 10, no. 3 (2020): 2354–77. http://dx.doi.org/10.1021/acscatal.9b04952.

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9

Bulacu, Monica, Xavier Périole, and Siewert J. Marrink. "In Silico Design of Robust Bolalipid Membranes." Biomacromolecules 13, no. 1 (2011): 196–205. http://dx.doi.org/10.1021/bm201454j.

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10

Avihoo, Assaf, Idan Gabdank, Michal Shapira, and Danny Barash. "In Silico Design of Small RNA Switches." IEEE Transactions on NanoBioscience 6, no. 1 (2007): 4–11. http://dx.doi.org/10.1109/tnb.2007.891894.

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11

Lewis, Daniel R., Vladyslav Kholodovych, Michael D. Tomasini, et al. "In silico design of anti-atherogenic biomaterials." Biomaterials 34, no. 32 (2013): 7950–59. http://dx.doi.org/10.1016/j.biomaterials.2013.07.011.

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12

Hamada, Michiaki. "In silico approaches to RNA aptamer design." Biochimie 145 (February 2018): 8–14. http://dx.doi.org/10.1016/j.biochi.2017.10.005.

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13

Rodrigo, Guillermo, Javier Carrera, and Santiago F. Elena. "Network design meets in silico evolutionary biology." Biochimie 92, no. 7 (2010): 746–52. http://dx.doi.org/10.1016/j.biochi.2010.04.003.

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14

Triveni, S., C. Naresh Babu, E. Bhargav, and M. Vijaya Jyothi. "in silico Design, ADME Prediction, Molecular Docking, Synthesis of Novel Triazoles, Indazoles & Aminopyridines and in vitro Evaluation of Antitubercular Activity." Asian Journal of Chemistry 32, no. 11 (2020): 2713–21. http://dx.doi.org/10.14233/ajchem.2020.22790.

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To design and synthesize novel triazoles, indazoles and aminopyridines from various (thiophene-2-yl)prop-2-en-1-one derivatives as antitubercular leads by in silico and in vitro methods. in silco Drug design, ADME prediction and molecular docking studies were performed to assess drug likeliness and antitubercular potential of all 30 novel triazoles, indazoles and aminopyridines. in silico Drug design studies revealed that the synthetic routes applied were appropriate according to the calculations of Swiss-ADME that measure synthetic accessibility. Most of the synthesized compounds found to have considerable binding score with enoyl ACP reductase enzyme of Mycobacterium tuberculosis. All the synthesized compounds were evaluated for antitubercular potential against Drug Resistant Mycobacterium tuberculosis H37Rv strain by Luciferase reporter assay method. Most of the synthesized compounds exhibited remarkable antitubercular potential against resistant strain.
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15

Pradhananga, Sarbendra, William Rayner, and Alan Berry. "De novo protein design: Evaluation of an in silico design method." Biochemical Society Transactions 28, no. 3 (2000): A69. http://dx.doi.org/10.1042/bst028a069c.

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16

HANDA, Susumu, Ken-ichiro TSUDA, Takashi IKEDA, and Yoshiaki TSUWA. "Integrated Environments for in silico Drug Design and Visualization." Journal of the Visualization Society of Japan 26, no. 101 (2006): 130–34. http://dx.doi.org/10.3154/jvs.26.130.

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17

Moura, Ana S., Amit K. Halder, and M. Natália DS Cordeiro. "From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling." Future Medicinal Chemistry 11, no. 17 (2019): 2313–31. http://dx.doi.org/10.4155/fmc-2018-0365.

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Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.
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18

Wang, Yulan, Jing Xing, Yuan Xu, et al. "In silico ADME/T modelling for rational drug design." Quarterly Reviews of Biophysics 48, no. 4 (2015): 488–515. http://dx.doi.org/10.1017/s0033583515000190.

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AbstractIn recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.
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19

Geris, L., R. Schugart, and H. Van Oosterwyck. "In silico design of treatment strategies in wound healing and bone fracture healing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1920 (2010): 2683–706. http://dx.doi.org/10.1098/rsta.2010.0056.

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Wound and bone fracture healing are natural repair processes initiated by trauma. Over the last decade, many mathematical models have been established to investigate the healing processes in silico , in addition to ongoing experimental work. In recent days, the focus of the mathematical models has shifted from simulation of the healing process towards simulation of the impaired healing process and the in silico design of treatment strategies. This review describes the most important causes of failure of the wound and bone fracture healing processes and the experimental models and methods used to investigate and treat these impaired healing cases. Furthermore, the mathematical models that are described address these impaired healing cases and investigate various therapeutic scenarios in silico . Examples are provided to illustrate the potential of these in silico experiments. Finally, limitations of the models and the need for and ability of these models to capture patient specificity and variability are discussed.
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20

Prabhakar, Yenamandra. "Chemical Structure Indices in In Silico Molecular Design." Scientia Pharmaceutica 76, no. 2 (2008): 101–32. http://dx.doi.org/10.3797/scipharm.0804-12.

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21

Pieraccini, Stefano, Giorgio Saladino, Graziella Cappelletti, et al. "In silico design of tubulin-targeted antimitotic peptides." Nature Chemistry 1, no. 8 (2009): 642–48. http://dx.doi.org/10.1038/nchem.401.

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22

Roy, Ankit, Sanjana Nair, Neeladri Sen, Neelesh Soni, and M. S. Madhusudhan. "In silico methods for design of biological therapeutics." Methods 131 (December 2017): 33–65. http://dx.doi.org/10.1016/j.ymeth.2017.09.008.

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23

Findeiß, Sven, Stefan Hammer, Michael T. Wolfinger, Felix Kühnl, Christoph Flamm, and Ivo L. Hofacker. "In silico design of ligand triggered RNA switches." Methods 143 (July 2018): 90–101. http://dx.doi.org/10.1016/j.ymeth.2018.04.003.

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24

Baumeier, Björn, Falk May, Christian Lennartz, and Denis Andrienko. "Challenges for in silico design of organic semiconductors." Journal of Materials Chemistry 22, no. 22 (2012): 10971. http://dx.doi.org/10.1039/c2jm30182b.

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25

Shiragannavar, Shilpa, and Shivakumar Madagi. "In Silico Vaccine Design against Mycoplasma hominis Infections." Biomedical and Pharmacology Journal 13, no. 1 (2020): 457–68. http://dx.doi.org/10.13005/bpj/1906.

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26

Marchand, Jean-Rémy, and Amedeo Caflisch. "In silico fragment-based drug design with SEED." European Journal of Medicinal Chemistry 156 (August 2018): 907–17. http://dx.doi.org/10.1016/j.ejmech.2018.07.042.

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27

Sanz-Herrera, J. A., E. Reina-Romo, and A. R. Boccaccini. "In silico design of magnesium implants: Macroscopic modeling." Journal of the Mechanical Behavior of Biomedical Materials 79 (March 2018): 181–88. http://dx.doi.org/10.1016/j.jmbbm.2017.12.016.

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28

Winiwarter, Susanne, Ernst Ahlberg, Edmund Watson, et al. "In silico ADME in drug design – enhancing the impact." ADMET and DMPK 6, no. 1 (2018): 15. http://dx.doi.org/10.5599/admet.6.1.470.

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<p>Each year the pharmaceutical industry makes thousands of compounds, many of which do not meet the desired efficacy or pharmacokinetic properties, describing the absorption, distribution, metabolism and excretion (ADME) behavior. Parameters such as lipophilicity, solubility and metabolic stability can be measured in high throughput in vitro assays. However, a compound needs to be synthesized in order to be tested. In silico models for these endpoints exist, although with varying quality. Such models can be used before synthesis and, together with a potency estimation, influence the decision to make a compound. In practice, it appears that often only one or two predicted properties are considered prior to synthesis, usually including a prediction of lipophilicity. While it is important to use all information when deciding which compound to make, it is somewhat challenging to combine multiple predictions unambiguously. This work investigates the possibility of combining in silico ADME predictions to define the minimum required potency for a specified human dose with sufficient confidence. Using a set of drug discovery compounds,in silico predictions were utilized to compare the relative ranking based on minimum potency calculation with the outcomes from the selection of lead compounds. The approach was also tested on a set of marketed drugs and the influence of the input parameters investigated.</p>
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29

Kleynhans, Janke, Hendrik Gerhardus Kruger, Theunis Cloete, Jan Rijn Zeevaart, and Thomas Ebenhan. "In Silico Modelling in the Development of Novel Radiolabelled Peptide Probes." Current Medicinal Chemistry 27, no. 41 (2020): 7048–63. http://dx.doi.org/10.2174/0929867327666200504082256.

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This review describes the usefulness of in silico design approaches in the design of new radiopharmaceuticals, especially peptide-based radiotracers (including peptidomimetics). Although not part of the standard arsenal utilized during radiopharmaceutical design, the use of in silico strategies is steadily increasing in the field of radiochemistry as it contributes to a more rational and scientific approach. The development of new peptide-based radiopharmaceuticals as well as a short introduction to suitable computational approaches are provided in this review. The first section comprises a concise overview of the three most useful computeraided drug design strategies used, namely i) a Ligand-based Approach (LBDD) using pharmacophore modelling, ii) a Structure-based Design Approach (SBDD) using molecular docking strategies and iii) Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) predictions. The second section summarizes the challenges connected to these computer-aided techniques and discusses successful applications of in silico radiopharmaceutical design in peptide-based radiopharmaceutical development, thereby improving the clinical procedure in Nuclear Medicine. Finally, the advances and future potential of in silico modelling as a design strategy is highlighted.
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30

Minuesa, Gerard, Cristina Alsina, Juan Antonio Garcia-Martin, Juan Carlos Oliveros, and Ivan Dotu. "MoiRNAiFold: a novel tool for complex in silico RNA design." Nucleic Acids Research 49, no. 9 (2021): 4934–43. http://dx.doi.org/10.1093/nar/gkab331.

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Abstract Novel tools for in silico design of RNA constructs such as riboregulators are required in order to reduce time and cost to production for the development of diagnostic and therapeutic advances. Here, we present MoiRNAiFold, a versatile and user-friendly tool for de novo synthetic RNA design. MoiRNAiFold is based on Constraint Programming and it includes novel variable types, heuristics and restart strategies for Large Neighborhood Search. Moreover, this software can handle dozens of design constraints and quality measures and improves features for RNA regulation control of gene expression, such as Translation Efficiency calculation. We demonstrate that MoiRNAiFold outperforms any previous software in benchmarking structural RNA puzzles from EteRNA. Importantly, with regard to biologically relevant RNA designs, we focus on RNA riboregulators, demonstrating that the designed RNA sequences are functional both in vitro and in vivo. Overall, we have generated a powerful tool for de novo complex RNA design that we make freely available as a web server (https://moiraibiodesign.com/design/).
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31

Richards, William D., Yan Wang, Lincoln J. Miara, Jae Chul Kim, and Gerbrand Ceder. "Design of Li1+2xZn1−xPS4, a new lithium ion conductor." Energy & Environmental Science 9, no. 10 (2016): 3272–78. http://dx.doi.org/10.1039/c6ee02094a.

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32

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

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Background: The application of in silico tools in the development of anti cancer drugs. Objective: The summing of different computer aided drug design approaches that have been applied in the development of anti cancer drugs. Methods: Structure based, ligand based, hybrid protein-ligand pharmacophore methods, Homology modeling, molecular docking aids in different steps of drug discovery pipeline with considerable saving in time and expenditure. In silico tools also find applications in the domain of cancer drug development. Results: Structure-based pharmacophore modeling aided in the identification of PUMA inhibitors, structure based approach with high throughput screening for the development of Bcl-2 inhibitors, to derive the most relevant protein-protein interactions, anti mitotic agents; I-Kappa-B Kinase β (IKK- β) inhibitor, screening of new class of aromatase inhibitors that can be important targets in cancer therapy. Conclusion: Application of computational methods in the design of anti cancer drugs was found to be effective.
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33

Buglak, Andrey A., Alexey V. Samokhvalov, Anatoly V. Zherdev, and Boris B. Dzantiev. "Methods and Applications of In Silico Aptamer Design and Modeling." International Journal of Molecular Sciences 21, no. 22 (2020): 8420. http://dx.doi.org/10.3390/ijms21228420.

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Aptamers are nucleic acid analogues of antibodies with high affinity to different targets, such as cells, viruses, proteins, inorganic materials, and coenzymes. Empirical approaches allow the design of in vitro aptamers that bind particularly to a target molecule with high affinity and selectivity. Theoretical methods allow significant expansion of the possibilities of aptamer design. In this study, we review theoretical and joint theoretical-experimental studies dedicated to aptamer design and modeling. We consider aptamers with different targets, such as proteins, antibiotics, organophosphates, nucleobases, amino acids, and drugs. During nucleic acid modeling and in silico design, a full set of in silico methods can be applied, such as docking, molecular dynamics (MD), and statistical analysis. The typical modeling workflow starts with structure prediction. Then, docking of target and aptamer is performed. Next, MD simulations are performed, which allows for an evaluation of the stability of aptamer/ligand complexes and determination of the binding energies with higher accuracy. Then, aptamer/ligand interactions are analyzed, and mutations of studied aptamers made. Subsequently, the whole procedure of molecular modeling can be reiterated. Thus, the interactions between aptamers and their ligands are complex and difficult to understand using only experimental approaches. Docking and MD are irreplaceable when aptamers are studied in silico.
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34

Rahal, Mahmoud, Mira Abdallah, Thanh-Tuân Bui, et al. "Design of new phenothiazine derivatives as visible light photoinitiators." Polymer Chemistry 11, no. 19 (2020): 3349–59. http://dx.doi.org/10.1039/d0py00497a.

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35

Wang, Ye, Chengkai Zhang, Song An, Xuexun Fang, and Dahai Yu. "Engineering substrate promiscuity in 2,4-dichlorophenol hydroxylase by in silico design." RSC Advances 8, no. 38 (2018): 21184–90. http://dx.doi.org/10.1039/c8ra03229g.

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36

Mortier, Jeremie, Christin Rakers, Raphael Frederick, and Gerhard Wolber. "Computational Tools for In Silico Fragment-Based Drug Design." Current Topics in Medicinal Chemistry 12, no. 17 (2012): 1935–43. http://dx.doi.org/10.2174/156802612804547371.

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37

Veljkovic, Nevena. "Recent In Silico Resources for Drug Design and Discovery." Current Medicinal Chemistry 26, no. 21 (2019): 3836–37. http://dx.doi.org/10.2174/092986732621190919104301.

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38

Burello, Enrico, and Gadi Rothenberg. "In Silico Design in Homogeneous Catalysis Using Descriptor Modelling." International Journal of Molecular Sciences 7, no. 9 (2006): 375–404. http://dx.doi.org/10.3390/i7090375.

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39

A. Ivanenkov, Yan, Mark S. Veselov, Vladimir A. Aladinskiy, et al. "In Silico Approaches to the Design of NS5A Inhibitors." Current Topics in Medicinal Chemistry 16, no. 12 (2016): 1383–91. http://dx.doi.org/10.2174/1568026616666151120113705.

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40

Varnek, A., D. Fourches, V. Solov'ev, O. Klimchuk, A. Ouadi, and I. Billard. "Successful “In Silico” Design of New Efficient Uranyl Binders." Solvent Extraction and Ion Exchange 25, no. 4 (2007): 433–62. http://dx.doi.org/10.1080/07366290701415820.

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41

Shah, Preksha, Jaymisha Mistry, Pedro A. Reche, Derek Gatherer, and Darren R. Flower. "In silico design of Mycobacterium tuberculosis epitope ensemble vaccines." Molecular Immunology 97 (May 2018): 56–62. http://dx.doi.org/10.1016/j.molimm.2018.03.007.

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42

Rinanda, T. "In Silico Studies in Antimicrobial Peptides Design and Development." IOP Conference Series: Earth and Environmental Science 305 (July 25, 2019): 012062. http://dx.doi.org/10.1088/1755-1315/305/1/012062.

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43

Řeha, David, Alexander A. Voityuk, and Sarah A. Harris. "An in Silico Design for a DNA Nanomechanical Switch." ACS Nano 4, no. 10 (2010): 5737–42. http://dx.doi.org/10.1021/nn1014038.

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44

Dauner, M., and K. Mauch. "In Silico Biotechnology: Analysis and Design of Cellular Networks." Chemie Ingenieur Technik 74, no. 5 (2002): 722. http://dx.doi.org/10.1002/1522-2640(200205)74:5<722::aid-cite722>3.0.co;2-b.

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45

Wijma, Hein J., Robert J. Floor, Sinisa Bjelic, Siewert J. Marrink, David Baker, and Dick B. Janssen. "Enantioselective Enzymes by Computational Design and In Silico Screening." Angewandte Chemie 127, no. 12 (2015): 3797–801. http://dx.doi.org/10.1002/ange.201411415.

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46

Sgobba, Miriam, and Giulio Rastelli. "Structure-Based and in silico Design of Hsp90 Inhibitors." ChemMedChem 4, no. 9 (2009): 1399–409. http://dx.doi.org/10.1002/cmdc.200900256.

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47

Wijma, Hein J., Robert J. Floor, Sinisa Bjelic, Siewert J. Marrink, David Baker, and Dick B. Janssen. "Enantioselective Enzymes by Computational Design and In Silico Screening." Angewandte Chemie International Edition 54, no. 12 (2015): 3726–30. http://dx.doi.org/10.1002/anie.201411415.

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48

Dash, Raju, Rasel Das, Md Junaid, Md Forhad Chowdhury Akash, Ashekul Islam, and SM Zahid Hosen. "In silico-based vaccine design against Ebola virus glycoprotein." Advances and Applications in Bioinformatics and Chemistry Volume 10 (March 2017): 11–28. http://dx.doi.org/10.2147/aabc.s115859.

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49

Hendling, Michaela, and Ivan Barišić. "In-silico Design of DNA Oligonucleotides: Challenges and Approaches." Computational and Structural Biotechnology Journal 17 (2019): 1056–65. http://dx.doi.org/10.1016/j.csbj.2019.07.008.

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50

Luna, Rodríguez-Salazar, Guevara-Pulido James, Morales-Mendoza Esteban, and Ibla Francisco. "In-silico design of peptide receptor for carboxyhemoglobin recognition." Informatics in Medicine Unlocked 14 (2019): 1–5. http://dx.doi.org/10.1016/j.imu.2019.01.003.

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