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Статті в журналах з теми "SILICO SCREENING"

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Автор: Grafiati

Опубліковано: 26 жовтня 2023

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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

Gallinger, Tom L., Samuel Y. Aboagye, Wiebke Obermann, Michael Weiss, Arnold Grünweller, Carlo Unverzagt, David L. Williams, Martin Schlitzer, and Simone Haeberlein. "First In Silico Screening of Insect Molecules for Identification of Novel Anti-Parasitic Compounds." Pharmaceuticals 15, no. 2 (January 19, 2022): 119. http://dx.doi.org/10.3390/ph15020119.

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Schistosomiasis is a neglected tropical disease caused by blood flukes of the genus Schistosoma. In silico screenings of compounds for the identification of novel anti-parasitic drug candidates have received considerable attention in recent years, including the screening of natural compounds. For the first time, we investigated molecules from insects, a rather neglected source in drug discovery, in an in silico screening approach to find novel antischistosomal compounds. Based on the Dictionary of Natural Products (DNP), we created a library of 1327 insect compounds suitable for molecular docking. A structure-based virtual screening against the crystal structure of a known druggable target in Schistosoma mansoni, the thioredoxin glutathione reductase (SmTGR), was performed. The top ten compounds predominantly originated from beetles and were predicted to interact particularly with amino acids in the doorstop pocket of SmTGR. For one compound from a jewel beetle, buprestin H, we tested and confirmed antischistosomal activity against adult and juvenile parasites in vitro. At concentrations with anti-parasitic activity, we could also exclude any unspecific cytotoxic activity against human HepG2 cells. This study highlights the potential of insect molecules for the identification of novel antischistosomal compounds. Our library of insect-derived molecules could serve not only as basis for future in silico screenings against additional target proteins of schistosomes, but also of other parasites.

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4

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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5

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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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

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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9

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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10

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

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Анотація:

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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11

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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12

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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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

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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15

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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16

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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17

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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18

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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19

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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20

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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21

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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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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Анотація:

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

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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24

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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25

Jukič, Marko, Sebastjan Kralj, Anja Kolarič, and Urban Bren. "Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening." Pharmaceuticals 16, no. 8 (August 17, 2023): 1170. http://dx.doi.org/10.3390/ph16081170.

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Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries’ display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.

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26

Islam, Rainul, Sumit Maji, Souparna Kabiraj, Umme Habib, Rohan Pal, Somenath Bhattacharya, Soumallya Chakraborty, and Dr Arin Bhattacharjee. "Role of in silico Drug Design in Pharmaceutical Sciences." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2358–67. http://dx.doi.org/10.22214/ijraset.2022.42836.

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Abstract: In silico drug design is the study to identify, develop, analyze, optimize drugs or biologically cum pharmaceutically active compounds by using computerized software programs as well web servers. In silico drug design is commonly known as computer aided drug design or CADD in short. This technique shows a vital role in preclinical drug design and development. CADD can improve the speed of drug design. It reduces time as well as total cost of the experiments. Potent cum suitable molecules are prepared after performing in silico drug design including CADD. Various applications like confirmation generation, homology modeling, multiple sequence alignment, molecular docking study, generation of Pharmacophores, virtual screening, de novo drug design, QSAR (Quantitative structure activity relationships) study, molecular modeling, in silico ADMET (Absorption, distribution, metabolism, excretion and toxicity) prediction of CADD has been implemented to design newer molecules. The current study focuses on different strategies cum approaches through computer aided drug designing applied to find potent, efficient and safe molecules in the field of drug discovery. Keywords: CADD, drug design, molecular docking, Pharmacophores, virtual screening, de novo drug design, QSAR, molecular modeling.

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27

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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28

Sekiou, O., W. Kherfane, M. Boumendjel, H. Cheniti, A. Benselhoub, and S. Bellucci. "Cathepsin inhibitors as potent inhibitors against SARS-CoV-2 main protease. In silico molecular screening and toxicity prediction." Ukrainian Biochemical Journal 95, no. 1 (April 26, 2023): 90–102. http://dx.doi.org/10.15407/ubj95.01.090.

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Анотація:

Since the emergence of the newly identified Coronavirus SARS-COV-2, no targeted therapeutic agents for COVID-19 treatment are available, and effective treatment options remain very limited. Successful crystallization of the SARS-CoV-2 main protease (Mpro, PDB-ID 6LU7) made possible the research on finding its potential inhibitors for the prevention of virus replication. To conduct molecular docking, we selected ten representatives of the Cathepsin inhibitors family as possible ligands with a high potential of binding the active site of SARS-CoV-2 main protease as a potential target. The results of molecular docking studies revealed that Ligand1 and Ligand2, with vina scores -8.8 and -8.7 kcal/mol for Mpro, respectively, were the most effective in binding. In silico prediction of physicochemical and toxicological behavior of assessed ligands approved the possibility of their use in clinical essays against SARS-COVID-19. Keywords: 6lu7, cathepsin inhibitors, COVID19, in silico prediction, main protease, molecular docking, SARS-COV-2

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29

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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30

Imai, Kento, Yuri Takeuchi, Kazunori Shimizu, and Hiroyuki Honda. "In Silico Screening of a Bile Acid Micelle Disruption Peptide for Oral Consumptions from Edible Peptide Database." Foods 10, no. 10 (October 18, 2021): 2496. http://dx.doi.org/10.3390/foods10102496.

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Recently, many bioactive peptides have been identified using bioinformatics tools. Previously, our group developed a method to screen dual-functional peptides that have direct intestinal delivery with porous silica gel and bile acid micelle disruption. However, newly designed peptides were not found in any storage protein. Therefore, in this study, in silico screening was performed using a 350,000 edible peptide library consisting of 4- to 7-mer independent peptides. As an initial screening, all edible peptides were applied to the random forest model to select predicted positive peptides. For a second screening, the peptides were assessed for the possibility of intestinal delivery using a 3D color map. From this approach, three novel dual-functional peptides, VYVFDE, WEFIDF, and VEEFYC were identified, and all of them were derived from storage proteins (legumin, myosin, and 11S globulin). In particular, VEEFYCS, in which a serine residue (S) is added to VEEFYC, was assumed to be released by thermolysin from the 11S-globulin derived from Ginkgo biloba by LC-MS/MS analysis. VEEFYCS was found to have suitable direct intestinal delivery and bile acid micelle disruption activity.

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31

Nguyen, Ha Thi, Thien-Y. Vu, A. Vijay Kumar, Vo Nguyen Huy Hoang, Pham Thi Ngoc My, Prashant S. Mandal та 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, № 47 (2021): 29385–93. http://dx.doi.org/10.1039/d1ra04407a.

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32

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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33

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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34

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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35

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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36

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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Анотація:

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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37

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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38

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

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39

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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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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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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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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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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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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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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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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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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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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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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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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