Thematische Bibliographien / DNA-Encoded Chemical Library

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Auswahl der wissenschaftlichen Literatur zum Thema „DNA-Encoded Chemical Library“

Autor: Grafiati

Veröffentlicht am 30. Juni 2021

Zuletzt aktualisiert am 28. Januar 2023

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Zeitschriftenartikel zum Thema "DNA-Encoded Chemical Library"

Dawadi, Surendra, Nicholas Simmons, Gabriella Miklossy, Kurt M. Bohren, John C. Faver, Melek Nihan Ucisik, Pranavanand Nyshadham, Zhifeng Yu und Martin M. Matzuk. „Discovery of potent thrombin inhibitors from a protease-focused DNA-encoded chemical library“. Proceedings of the National Academy of Sciences 117, Nr. 29 (08.07.2020): 16782–89. http://dx.doi.org/10.1073/pnas.2005447117.

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DNA-encoded chemical libraries are collections of compounds individually coupled to unique DNA tags serving as amplifiable identification barcodes. By bridging split-and-pool combinatorial synthesis with the ligation of unique encoding DNA oligomers, million- to billion-member libraries can be synthesized for use in hundreds of healthcare target screens. Although structural diversity and desirable molecular property ranges generally guide DNA-encoded chemical library design, recent reports have highlighted the utility of focused DNA-encoded chemical libraries that are structurally biased for a class of protein targets. Herein, a protease-focused DNA-encoded chemical library was designed that utilizes chemotypes known to engage conserved catalytic protease residues. The three-cycle library features functional moieties such as guanidine, which interacts strongly with aspartate of the protease catalytic triad, as well as mild electrophiles such as sulfonamide, urea, and carbamate. We developed a DNA-compatible method for guanidinylation of amines and reduction of nitriles. Employing these optimized reactions, we constructed a 9.8-million-membered DNA-encoded chemical library. Affinity selection of the library with thrombin, a common protease, revealed a number of enriched features which ultimately led to the discovery of a 1 nM inhibitor of thrombin. Thus, structurally focused DNA-encoded chemical libraries have tremendous potential to find clinically useful high-affinity hits for the rapid discovery of drugs for targets (e.g., proteases) with essential functions in infectious diseases (e.g., severe acute respiratory syndrome coronavirus 2) and relevant healthcare conditions (e.g., male contraception).

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Reddavide, Francesco V., Meiying Cui, Weilin Lin, Naiqiang Fu, Stephan Heiden, Helena Andrade, Michael Thompson und Yixin Zhang. „Second generation DNA-encoded dynamic combinatorial chemical libraries“. Chemical Communications 55, Nr. 26 (2019): 3753–56. http://dx.doi.org/10.1039/c9cc01429b.

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Onda, Yuichi, Gabriele Bassi, Abdullah Elsayed, Franziska Ulrich, Sebastian Oehler, Louise Plais, Jörg Scheuermann und Dario Neri. „A DNA‐Encoded Chemical Library Based on Peptide Macrocycles“. Chemistry – A European Journal 27, Nr. 24 (18.03.2021): 7160–67. http://dx.doi.org/10.1002/chem.202005423.

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Dumelin, Christoph E., Jörg Scheuermann, Samu Melkko und Dario Neri. „Selection of Streptavidin Binders from a DNA-Encoded Chemical Library“. Bioconjugate Chemistry 17, Nr. 2 (März 2006): 366–70. http://dx.doi.org/10.1021/bc050282y.

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Faver, John C., Kevin Riehle, David R. Lancia, Jared B. J. Milbank, Christopher S. Kollmann, Nicholas Simmons, Zhifeng Yu und Martin M. Matzuk. „Quantitative Comparison of Enrichment from DNA-Encoded Chemical Library Selections“. ACS Combinatorial Science 21, Nr. 2 (23.01.2019): 75–82. http://dx.doi.org/10.1021/acscombsci.8b00116.

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Dumelin, Christoph E, Sabrina Trüssel, Fabian Buller, Eveline Trachsel, Frank Bootz, Yixin Zhang, Luca Mannocci et al. „A Portable Albumin Binder from a DNA-Encoded Chemical Library“. Angewandte Chemie 120, Nr. 17 (14.04.2008): 3240–45. http://dx.doi.org/10.1002/ange.200704936.

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Stress, Cedric J., Basilius Sauter, Lukas A. Schneider, Timothy Sharpe und Dennis Gillingham. „A DNA‐Encoded Chemical Library Incorporating Elements of Natural Macrocycles“. Angewandte Chemie International Edition 58, Nr. 28 (08.07.2019): 9570–74. http://dx.doi.org/10.1002/anie.201902513.

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Edwards, Paul. „Design and synthesis of a novel DNA-encoded chemical library“. Drug Discovery Today 15, Nr. 15-16 (August 2010): 690–91. http://dx.doi.org/10.1016/j.drudis.2010.06.013.

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Dumelin, Christoph E, Sabrina Trüssel, Fabian Buller, Eveline Trachsel, Frank Bootz, Yixin Zhang, Luca Mannocci et al. „A Portable Albumin Binder from a DNA-Encoded Chemical Library“. Angewandte Chemie International Edition 47, Nr. 17 (14.04.2008): 3196–201. http://dx.doi.org/10.1002/anie.200704936.

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Shi, Ying, Yan-ran Wu, Jian-qiang Yu, Wan-nian Zhang und Chun-lin Zhuang. „DNA-encoded libraries (DELs): a review of on-DNA chemistries and their output“. RSC Advances 11, Nr. 4 (2021): 2359–76. http://dx.doi.org/10.1039/d0ra09889b.

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We summarize a series of novel DNA-compatible chemistry reactions for DNA-encoded chemical library (DEL) building blocks and analyse the druggability of screened hit molecules via DELs in the past five years.

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Dissertationen zum Thema "DNA-Encoded Chemical Library"

Geylan, Gökçe. „Training Machine Learning-based QSAR models with Conformal Prediction on Experimental Data from DNA-Encoded Chemical Libraries“. Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447354.

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DNA-encoded chemical libraries (DEL) allows an exhaustive chemical space sampling with a large-scale data consisting of compounds produced through combinatorial synthesis. This novel technology was utilized in the early drug discovery stages for robust hit identification and lead optimization. In this project, the aim was to build a Machine Learning- based QSAR model with conformal prediction for hit identification on two different target proteins, the DEL was assayed on. An initial investigation was conducted on a pilot project with 1000 compounds and the analyses and the conclusions drawn from this part were later applied to a larger dataset with 1.2 million compounds. With this classification model, the prediction of the compound activity in the DEL as well as in an external dataset was aimed to be analyzed with identification of the top hits to evaluate model’s performance and applicability. Support Vector Machine (SVM) and Random Forest (RF) models were built on both the pilot and the main datasets with different descriptor sets of Signature Fingerprints, RDKIT and CDK. In addition, an Autoencoder was used to supply data-driven descriptors on the pilot data as well. The Libsvm and the Liblinear implementations were explored and compared based on the models’ performances. The comparisons were made by considering the key concepts of conformal prediction such as the trade-off between validity and efficiency, observed fuzziness and the calibration against a range of significance levels. The top hits were determined by two sorting methods, credibility and p-value differences between the binary classes. The assignment of correct single-labels to the true actives over a wide range of significance levels regardless of the similarity of the test compounds to the training set was confirmed for the models. Furthermore, an accumulation of these true actives in the models’ top hit selections was observed according to the latter sorting method and additional investigations on the similarity and the building block enrichments in the top 50 and 100 compounds were conducted. The Tanimoto similarity demonstrated the model’s predictive power in selecting structurally dissimilar compounds while the building block enrichment analysis showed the selectivity of the binding pocket where the target protein B was determined to be more selective. All of these comparison methods enabled an extensive study on the model evaluation and performance. In conclusion, the Liblinear model with the Signature Fingerprints was concluded to give the best model performance for both the pilot and the main datasets with the considerations of the model performances and the computational power requirements. However, an external set prediction was not successful due to the low structural diversity in the DEL which the model was trained on.

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Bücher zum Thema "DNA-Encoded Chemical Library"

Jr, Robert A. Goodnow. A Handbook for DNA-Encoded Chemistry: Theory and Applications for Exploring Chemical Space and Drug Discovery. Wiley, 2014.

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Buchteile zum Thema "DNA-Encoded Chemical Library"

Yao, Gang, Xiaojie Lu, Chris Phelps und Ghotas Evindar. „Chapter 7. DNA-encoded Library Technology (ELT)“. In Chemical and Biological Synthesis, 153–83. Cambridge: Royal Society of Chemistry, 2018. http://dx.doi.org/10.1039/9781788012805-00153.

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Melkko, Samu, und Johannes Ottl. „Keeping the Promise? An Outlook on DNA Chemical Library Technology“. In A Handbook for DNA-Encoded Chemistry, 427–34. Hoboken, NJ: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118832738.ch19.

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Zhu, Zhengrong. „Selection Method of DNA-Encoded Chemical Library for Irreversible Covalent Binders“. In Methods in Molecular Biology, 165–72. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2545-3_20.

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