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Journal articles on the topic 'In silico screening'

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Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Recanatini, Maurizio, Giovanni Bottegoni, and Andrea Cavalli. "In silico antitarget screening." Drug Discovery Today: Technologies 1, no. 3 (December 2004): 209–15. http://dx.doi.org/10.1016/j.ddtec.2004.10.004.

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2

Rudisser, Simon, and Wolfgang Jahnke. "NMR and In silico Screening." Combinatorial Chemistry & High Throughput Screening 5, no. 8 (December 1, 2002): 591–603. http://dx.doi.org/10.2174/1386207023329987.

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3

Seifert, Markus H. J., Kristina Wolf, and Daniel Vitt. "Virtual high-throughput in silico screening." BIOSILICO 1, no. 4 (September 2003): 143–49. http://dx.doi.org/10.1016/s1478-5382(03)02359-x.

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4

Reddy, Bandi Deepa, and Ch M. Kumari Chitturi. "Screening and Identification of Microbial Derivatives for Inhibiting Legumain: An In silico Approach." Journal of Pure and Applied Microbiology 12, no. 3 (September 30, 2018): 1623–30. http://dx.doi.org/10.22207/jpam.12.3.69.

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5

Zaini, Vikra Ardiansyah, Purwantiningsih Sugita, Luthfan Irfana, and Suminar Setiati Achmadi. "In Silico Screening Anticancer of Six Triterpenoids toward miR-494 and TNF-α Targets." Jurnal Kimia Sains dan Aplikasi 23, no. 4 (April 7, 2020): 117–23. http://dx.doi.org/10.14710/jksa.23.4.117-123.

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Hepatocellular carcinoma (HCC) accounts for up to 90% of all primary liver cancers worldwide. Cinobufagin is recognized to inhibit miR-494 as the HCC target. Increased expression of TNF-α results in an inadequate response to liver anticancer drugs. The models in this study were cinobufagin, cycloartenol, and ethyl acetate fractions of Ganoderma lucidum, 2–5. Seven docking targets in this study were Akt, ERK1, ERK2, PI3K, TNF-α, TNFR1, and TNFR2. Cycloartenol and compound 4 comply with Veber’s rules, Lipinski’s rule of 5, and demonstrate moderate toxicity. The action implies a potential docking target since it produces bond affinities with the compound 2–5 that agree with the IC50 in the literature, which is based on in vitro experiments. Akt as a receptor target is AZD5363. Cycloartenol shows a low ability to inhibit Akt. Conversely, compound 4 inhibits the Akt better than that of cycloartenol, although it is not as good as cinobufagin and AZD5363. Therefore, compound 4, a triterpenoid with a basic framework of lanostane has the potential to be an anticancer candidate for the liver.

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6

Ma, Dik-Lung, Victor Pui-Yan Ma, Daniel Shiu-Hin Chan, Ka-Ho Leung, Hai-Jing Zhong, and Chung-Hang Leung. "In silico screening of quadruplex-binding ligands." Methods 57, no. 1 (May 2012): 106–14. http://dx.doi.org/10.1016/j.ymeth.2012.02.001.

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7

Lin, Li-Chiang, Adam H. Berger, Richard L. Martin, Jihan Kim, Joseph A. Swisher, Kuldeep Jariwala, Chris H. Rycroft, et al. "In silico screening of carbon-capture materials." Nature Materials 11, no. 7 (May 27, 2012): 633–41. http://dx.doi.org/10.1038/nmat3336.

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8

Vidal, David, and Jordi Mestres. "In Silico Receptorome Screening of Antipsychotic Drugs." Molecular Informatics 29, no. 6-7 (July 9, 2010): 543–51. http://dx.doi.org/10.1002/minf.201000055.

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9

Arvidson, Kirk B., Luis G. Valerio, Marilyn Diaz, and Ronald F. Chanderbhan. "In Silico Toxicological Screening of Natural Products." Toxicology Mechanisms and Methods 18, no. 2-3 (January 2008): 229–42. http://dx.doi.org/10.1080/15376510701856991.

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10

Fukunishi, Yoshifumi, Satoru Kubota, and Haruki Nakamura. "Noise Reduction Method for Molecular Interaction Energy: Application to in Silico Drug Screening and in Silico Target Protein Screening." Journal of Chemical Information and Modeling 46, no. 5 (July 28, 2006): 2071–84. http://dx.doi.org/10.1021/ci060152z.

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11

Fukunishi, Yoshifumi, Yoshiaki Mikami, Satoru Kubota, and Haruki Nakamura. "Multiple target screening method for robust and accurate in silico ligand screening." Journal of Molecular Graphics and Modelling 25, no. 1 (September 2006): 61–70. http://dx.doi.org/10.1016/j.jmgm.2005.11.006.

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12

Stepanovs, Dmitrijs, Ma̅ra Jure, Liudmila N. Kuleshova, Detlef W. M. Hofmann, and Anatoly Mishnev. "Cocrystals of Pentoxifylline: In Silico and Experimental Screening." Crystal Growth & Design 15, no. 8 (July 7, 2015): 3652–60. http://dx.doi.org/10.1021/acs.cgd.5b00185.

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13

Luo, Cheng, Peng Xie, and Ronen Marmorstein. "Identification of BRAF Inhibitors through In Silico Screening." Journal of Medicinal Chemistry 51, no. 19 (October 9, 2008): 6121–27. http://dx.doi.org/10.1021/jm800539g.

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14

Li, Albert P., and Matthew Segall. "Early ADME/Tox studies and in silico screening." Drug Discovery Today 7, no. 1 (January 2002): 25–27. http://dx.doi.org/10.1016/s1359-6446(01)02117-1.

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15

Lindert, Steffen, Wei Zhu, Yi‐Liang Liu, Ran Pang, Eric Oldfield, and J. Andrew McCammon. "Farnesyl Diphosphate Synthase Inhibitors from In Silico Screening." Chemical Biology & Drug Design 81, no. 6 (May 25, 2013): 742–48. http://dx.doi.org/10.1111/cbdd.12121.

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16

Byler, KG, and WN Setzer. "In-silico screening for anti-zika virus phytochemicals." Planta Medica 81, S 01 (December 14, 2016): S1—S381. http://dx.doi.org/10.1055/s-0036-1596272.

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17

Takaya, Daisuke, Mayuko Takeda-Shitaka, Genki Terashi, Kazuhiko Kanou, Mitsuo Iwadate, and Hideaki Umeyama. "Bioinformatics Based Ligand-Docking and in-Silico Screening." CHEMICAL & PHARMACEUTICAL BULLETIN 56, no. 5 (2008): 742–44. http://dx.doi.org/10.1248/cpb.56.742.

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18

Spiliotopoulos, Dimitrios, and Amedeo Caflisch. "Fragment-based in silico screening of bromodomain ligands." Drug Discovery Today: Technologies 19 (March 2016): 81–90. http://dx.doi.org/10.1016/j.ddtec.2016.06.003.

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19

Feiler, Christian, Di Mei, Bahram Vaghefinazari, Tim Würger, Robert H. Meißner, Bérengère J. C. Luthringer-Feyerabend, David A. Winkler, Mikhail L. Zheludkevich, and Sviatlana V. Lamaka. "In silico screening of modulators of magnesium dissolution." Corrosion Science 163 (February 2020): 108245. http://dx.doi.org/10.1016/j.corsci.2019.108245.

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20

Byler, Kendall G., Ifedayo Victor Ogungbe, and William N. Setzer. "In-silico screening for anti-Zika virus phytochemicals." Journal of Molecular Graphics and Modelling 69 (September 2016): 78–91. http://dx.doi.org/10.1016/j.jmgm.2016.08.011.

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21

Stoner, Chad, Mathew Troutman, Hua Gao, Kjell Johnson, Charles Stankovic, Joanne Brodfuehrer, Eric Gifford, and Man Chang. "Moving in Silico Screening into Practice: A Minimalist Approach to Guide Permeability Screening!!" Letters in Drug Design & Discovery 3, no. 8 (October 1, 2006): 575–81. http://dx.doi.org/10.2174/157018006778194736.

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22

Tang, Min, Yang Fu, Ying Fan, Ming-Shui Fu, Zhi Zheng, and Xun Xu. "In-silico design of novel myocilin inhibitors for glaucoma therapy." Tropical Journal of Pharmaceutical Research 16, no. 10 (November 15, 2017): 2527–33. http://dx.doi.org/10.4314/tjpr.v16i10.29.

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Purpose: To explore newer computational approaches in the design of novel myocilin inhibitors for the treatment of glaucoma.Methods: An in-silico virtual screening technique based on simulation of molecular docking was utilised to design a novel myocilin inhibitors for the treatment of glaucoma. The designed novel molecules were theoretically evaluated to predict their pharmacokinetic properties and toxic effects. Lead molecules were screened out in virtual screening technique on the basis of low binding energies obtained in AutoDock based molecular docking simulation.Results: Out of ten top lead compounds screened, ZINC01729523 and ZINC04692015 were promising, having shown potent inhibition of myocilin, good pharmacokinetic properties and absence of any toxic effects.Conclusion: In-silico virtual screening of molecular libraries containing a large number of ligands is very useful for short-listing of potential lead molecules for further structure-based discovery of antiglaucoma-drugs.Keywords: Glaucoma, Myocilin, Docking, Virtual-screening, Autodock, Ligand, Drug design

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23

Decesari, Stefano, Simona Kovarich, Manuela Pavan, Arianna Bassan, Andrea Ciacci, and David Topping. "Evaluating the mutagenic potential of aerosol organic compounds using informatics-based screening." Atmospheric Chemistry and Physics 18, no. 3 (February 16, 2018): 2329–40. http://dx.doi.org/10.5194/acp-18-2329-2018.

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Abstract. Whilst general policy objectives to reduce airborne particulate matter (PM) health effects are to reduce exposure to PM as a whole, emerging evidence suggests that more detailed metrics associating impacts with different aerosol components might be needed. Since it is impossible to conduct toxicological screening on all possible molecular species expected to occur in aerosol, in this study we perform a proof-of-concept evaluation on the information retrieved from in silico toxicological predictions, in which a subset (N = 104) of secondary organic aerosol (SOA) compounds were screened for their mutagenicity potential. An extensive database search showed that experimental data are available for 13 % of the compounds, while reliable predictions were obtained for 82 %. A multivariate statistical analysis of the compounds based on their physico-chemical, structural, and mechanistic properties showed that 80 % of the compounds predicted as mutagenic were grouped into six clusters, three of which (five-membered lactones from monoterpene oxidation, oxygenated multifunctional compounds from substituted benzene oxidation, and hydroperoxides from several precursors) represent new candidate groups of compounds for future toxicological screenings. These results demonstrate that coupling model-generated compositions to in silico toxicological screening might enable more comprehensive exploration of the mutagenic potential of specific SOA components.

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24

Balouch, Martin, Martin Šrejber, Marek Šoltys, Petra Janská, František Štěpánek, and Karel Berka. "In silico screening of drug candidates for thermoresponsive liposome formulations." Molecular Systems Design & Engineering 6, no. 5 (2021): 368–80. http://dx.doi.org/10.1039/d0me00160k.

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In silico methodology for compound suitability for liposomal formulation has been developed. Water–lipid partitioning and permeation of candidate compounds from the DrugBank were calculated, and the most appropriate targets validated experimentally.

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25

Ramanathan, T., and V. Manigandan. "In silico Screening of Cyclooxygenase Inhibitory Molecules from Margroves." Trends in Bioinformatics 7, no. 1 (January 1, 2014): 13–18. http://dx.doi.org/10.3923/tb.2014.13.18.

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26

TAKADA, Toshikazu. "In Silico Screening for New Drug and Computational Chemistry." Journal of Computer Chemistry, Japan 6, no. 3 (2007): 159–66. http://dx.doi.org/10.2477/jccj.6.159.

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27

M. Patil, Vaishali, Neeraj Masand, and Satya P. Gupta. "GENIUS In Silico Screening Technology for HCV Drug Discovery." Current Drug Discovery Technologies 13, no. 4 (November 16, 2016): 189–98. http://dx.doi.org/10.2174/1570163813666161006113011.

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28

Daggupati, Trinath, Rishika Pamanji, and Suneetha Yeguvapalli. "In silico screening and identification of potential GSK3β inhibitors." Journal of Receptors and Signal Transduction 38, no. 4 (June 27, 2018): 279–89. http://dx.doi.org/10.1080/10799893.2018.1478854.

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29

Krishna, Rajamani, and Jasper M. van Baten. "In silico screening of zeolite membranes for CO2 capture." Journal of Membrane Science 360, no. 1-2 (September 2010): 323–33. http://dx.doi.org/10.1016/j.memsci.2010.05.032.

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30

Senderowitz, Hanoch, and Yael Marantz. "G Protein-Coupled Receptors: Target-Based In Silico Screening." Current Pharmaceutical Design 15, no. 35 (December 1, 2009): 4049–68. http://dx.doi.org/10.2174/138161209789824821.

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31

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 (January 30, 2015): 3797–801. http://dx.doi.org/10.1002/ange.201411415.

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32

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 (February 4, 2015): 3726–30. http://dx.doi.org/10.1002/anie.201411415.

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33

Desler, Claus, Prashanth Suravajhala, May Sanderhoff, Merete Rasmussen, and Lene Rasmussen. "In Silico screening for functional candidates amongst hypothetical proteins." BMC Bioinformatics 10, no. 1 (2009): 289. http://dx.doi.org/10.1186/1471-2105-10-289.

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34

Lorenzen, Stephan, Mathias Dunkel, and Robert Preissner. "In silico screening of drug databases for TSE inhibitors." Biosystems 80, no. 2 (May 2005): 117–22. http://dx.doi.org/10.1016/j.biosystems.2004.10.004.

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35

Merlitz, H., B. Burghardt, and W. Wenzel. "Impact of receptor conformation on in silico screening performance." Chemical Physics Letters 390, no. 4-6 (June 2004): 500–505. http://dx.doi.org/10.1016/j.cplett.2004.04.074.

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36

Sánchez, Horacio E. "High Throughput In-silico Screening Against Flexible Protein Receptors." Biophysical Journal 96, no. 3 (February 2009): 86a. http://dx.doi.org/10.1016/j.bpj.2008.12.348.

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37

Zaghlool Al-Khayyat, Mohammed. "In silico screening of natural products targeting chorismate synthase." Innovaciencia Facultad de Ciencias Exactas Físicas y Naturales 7, no. 1 (October 25, 2019): 1–9. http://dx.doi.org/10.15649/2346075x.505.

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Introduction: Chorismate synthase catalyzes the final step in shikimate acid pathway involved in synthesis of aromatic compounds in bacteria.This enzyme can be a possible molecular target for design of antibiotics. Materials and Methods: Homology modeling and molecular dockingwere performed to screen about one hundred natural compounds in order to find inhibitors of enzymes as a possible new target. A model wasbuilt by SWISS-MODEL and its quality was assessed by ERRAT, ProSA, Rampage and MolProbity servers. Docking experiments were performedand pharmacokinetics and toxicities were studied by admetSAR. Results: The predicted model was reliable to be used in docking experiments.Amentoflavone had the highest binding affinity of -10.0 Kcal/mol. Probabilities indicated that rotenone may inhibit P-glycoprotein I, hinokiflavone and silybin may inhibit P-glycoprotein II, while taspine acts on both types of P-glycoproteins. Amentofalavone, hinokiflavone, rotenone and silybin have a probability of inhibiting cytochromes that are involved in oxidation stage of metabolism. Conclusions: These compounds had binding affinities towards FMN binding site of the enzyme model and may be considered in the research for new antibacterial agents but only when their drug interactions are fully investigated.

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38

KAMINUMA, ELI, NAOHIKO HEIDA, TAKESHI YOSHIZUMI, MIKI NAKAZAWA, MINAMI MATSUI, and TETSURO TOYODA. "IN SILICO PHENOTYPIC SCREENING METHOD OF MUTANTS BASED ON STATISTICAL MODELING OF GENETICALLY MIXED SAMPLES." Journal of Bioinformatics and Computational Biology 03, no. 06 (December 2005): 1281–93. http://dx.doi.org/10.1142/s0219720005001557.

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In comprehensive functional genomics projects, systematic analysis of phenotypes is vital. However, conventional phenotypic screening is done mainly by imprecise visual observation of qualitative traits, and, therefore, in silico screening techniques for quantitative traits are required. In this report, we propose in silico phenotypic screening method that utilizes a Gaussian mixture model for the trait distribution in the offspring of a mutagenized line and the likelihood ratio test between the estimated Gaussian mixture model and the wild-type single Gaussian model. In order to evaluate the proposed method, we performed a screening experiment using real trait data of Arabidopsis. In this experiment, the proposed screening method properly distinguished the mutant line from the wild-type line. Furthermore, we conducted power analysis of the proposed method and two conventional methods under various simulated conditions of sample size and distribution of trait frequency. The result of the power analysis confirmed the effectiveness of the proposed method compared to the conventional methods.

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39

Deyon-Jung, Laurence, Christophe Morice, Florence Chéry, Julie Gay, Thierry Langer, Marie-Céline Frantz, Roger Rozot, and Maria Dalko-Csiba. "Fragment pharmacophore-based in silico screening: a powerful approach for efficient lead discovery." MedChemComm 7, no. 3 (2016): 506–11. http://dx.doi.org/10.1039/c5md00444f.

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40

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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41

Payra, Soumen, Arijit Saha, Chia-Ming Wu, Balaranjan Selvaratnam, Thorn Dramstad, Luther Mahoney, Sant Kumar Verma, Suresh Thareja, Ranjit Koodali, and Subhash Banerjee. "Fe–SBA-15 catalyzed synthesis of 2-alkoxyimidazo[1,2-a]pyridines and screening of their in silico selectivity and binding affinity to biological targets." New Journal of Chemistry 40, no. 11 (2016): 9753–60. http://dx.doi.org/10.1039/c6nj02134d.

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42

Nguyen, Ha Thi, Thien-Y. Vu, A. Vijay Kumar, Vo Nguyen Huy Hoang, Pham Thi Ngoc My, Prashant S. Mandal, and Vinay Bharadwaj Tatipamula. "N-Aryl iminochromenes inhibit cyclooxygenase enzymes via π–π stacking interactions and present a novel class of anti-inflammatory drugs." RSC Advances 11, no. 47 (2021): 29385–93. http://dx.doi.org/10.1039/d1ra04407a.

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43

Zang, Jie, Shangzhi Ma, Cuizhe Wang, Gang Guo, Liangxue Zhou, Xing Tian, Mengying Lv, Jun Zhang, and Bo Han. "Screening for active constituents in Turkish galls against ulcerative colitis by mass spectrometry guided preparative chromatography strategy:in silico,in vitroandin vivostudy." Food & Function 9, no. 10 (2018): 5124–38. http://dx.doi.org/10.1039/c8fo01439f.

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44

Yim, Sung-Kun, Kian Kim, SangHo Chun, TaeHawn Oh, WooHuk Jung, KyooJin Jung, and Chul-Ho Yun. "Screening of Human CYP1A2 and CYP3A4 Inhibitors from Seaweed In Silico and In Vitro." Marine Drugs 18, no. 12 (November 29, 2020): 603. http://dx.doi.org/10.3390/md18120603.

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Phenolic compounds and carotenoids are potential inhibitors of cytochrome P450s. Sixteen known compounds, phenolic compounds and carotenoids from seaweed were examined for potential inhibitory capacity against CYP1A2 and CYP3A4 in silico and in vitro. Morin, quercetin, and fucoxanthin inhibited the enzyme activity of CYP1A2 and CYP3A4 in a dose-dependent manner. The IC50 values of morin, quercetin, and fucoxanthin were 41.8, 22.5, and 30.3 μM for CYP1A2 and 86.6, 16.1, and 24.4 μM for CYP3A4, respectively. Siphonaxanthin and hesperidin did not show any significant effect on CYP1A2, but they slightly inhibited CYP3A4 activity at high concentrations. In silico modeling of CYP’s binding site revealed that the potential inhibitors bound in the cavity located above the distal surface of the heme prosthetic group through the 2a or 2f channel of CYPs. This study presents an approach for quickly predicting CYP inhibitory activity and shows the potential interactions of compounds and CYPs through in silico modeling.

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45

Zhong, Hai-Jing, Bo Ra Lee, Joshua William Boyle, Wanhe Wang, Dik-Lung Ma, Philip Wai Hong Chan, and Chung-Hang Leung. "Structure-based screening and optimization of cytisine derivatives as inhibitors of the menin–MLL interaction." Chemical Communications 52, no. 34 (2016): 5788–91. http://dx.doi.org/10.1039/c6cc01079b.

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46

Di Natale, Concetta, Giorgia Celetti, Pasqualina Liana Scognamiglio, Chiara Cosenza, Edmondo Battista, Filippo Causa, and Paolo A. Netti. "Molecularly endowed hydrogel with an in silico-assisted screened peptide for highly sensitive small molecule harvesting." Chemical Communications 54, no. 72 (2018): 10088–91. http://dx.doi.org/10.1039/c8cc04943b.

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47

Dharani, J., and S. Ravi. "in silico ADMET Screening of Compounds Present in Cyanthillium cinereum (L.) H. Rob." Asian Journal of Chemistry 32, no. 6 (2020): 1421–26. http://dx.doi.org/10.14233/ajchem.2020.22569.

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Drug development involves assessment of absorption, distribution, metabolism, excretion and toxicity (ADMET) increasingly earlier in the discovery process. in silico ADMET studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at only the promising compounds. To this end, several in silico approaches for predicting ADMET properties of compounds from their chemical structure have been developed, ranging from data-based approaches. In this study, ADMET prediction has been done for 20 compounds from the plant Cyanthillium cinereum extracts. Some of the compounds were predicted to be non-toxic.

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48

Rifai, Eko Aditya, Hayun Hayun, and Arry Yanuar. "IN SILICO SCREENING OF ANTIMALARIAL FROM INDONESIAN MEDICINAL PLANTS DATABASE TO PLASMEPSIN TARGET." Asian Journal of Pharmaceutical and Clinical Research 10, no. 17 (October 1, 2017): 130. http://dx.doi.org/10.22159/ajpcr.2017.v10s5.23115.

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Objective: Malaria is a disease that impacts millions of people annually. Among the enzymes, plasmepsin is the main enzyme in the plasmodium life cycle that degrades hemoglobin during the erythrocytic phase in the food vacuole. Recently, pharmaceutical industries have been trying to develop therapeutic agents that can cure malaria through the discovery of new plasmepsin inhibitor compounds. One of the developing approaches is the in silico method.Methods: The chosen in silico screening method in this experiment is a structure-based screening using GOLD software and the Indonesian medicinal plants database.Results: From ten in silico screening runs, three of the compounds always ranked in the top ten. These three compounds are trimyristin, cyanidin 3,5-di-(6-malonylglucoside), and isoscutellarein 4’-methyl ether 8-(6”-n-butylglucuronide). Another compound that emerged with high frequency is cyanidin 3,5-di-(6-malonylglucoside). Conclusions: Based on the results obtained from this screening, 11 inhibitor candidates are expected to be developed as antimalarial. These compounds are trimyristin; cyanidin 3,5-di-(6-malonylglucoside); isoscutellarein 4’-methyl ether 8-(6”-n-butylglucuronide); cyanidin 3-(6”-malonylglucoside)-5- glucoside; multifloroside; delphinidin 3-(2-rhamnosyl-6-malonylglucoside); delphinidin 3-(6-malonylglucoside)-3’,5’-di-(6-p-coumaroylglucoside); cyanidin 3-[6-(6-sinapylglucosyl)-2-xylosylgalactoside; kaempferol 3-glucosyl-(1-3)-rhamnosyl-(1-6)-galactoside; sanggenofuran A; and lycopene with a GOLD score range from 78.4647 to 98.2836. Two of them, Asp34 and Asp214, bind with all residues in the catalytic site of plasmepsin.

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49

Aoki, Shunsuke, Junichi Taira, Takashi Ito, Hitomi Nakatani, Tomohiro Umei, Hiroki Baba, Shotaro Kawashima, Taira Maruoka, Hideyuki Komatsu, and Hiroshi Sakamoto. "In silico structure-based drug screening of novel antimycobacterial pharmacophores by DOCK-GOLD tandem screening." International Journal of Mycobacteriology 6, no. 2 (2017): 142. http://dx.doi.org/10.4103/ijmy.ijmy_24_17.

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50

Fukunishi, Yoshifumi, and Masami Lintuluoto. "Development of Chemical Compound Libraries for In Silico Drug Screening." Current Computer Aided-Drug Design 6, no. 2 (June 1, 2010): 90–102. http://dx.doi.org/10.2174/157340910791202450.

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