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Готові списки джерел за темами / Novel class discovery / Статті в журналах

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Статті в журналах з теми "Novel class discovery"

Автор: Grafiati

Опубліковано: 9 листопада 2024

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1

Chen, Guangyao, Peixi Peng, Yangru Huang, Mengyue Geng, and Yonghong Tian. "Adaptive Discovering and Merging for Incremental Novel Class Discovery." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (March 24, 2024): 11276–84. http://dx.doi.org/10.1609/aaai.v38i10.29006.

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One important desideratum of lifelong learning aims to discover novel classes from unlabelled data in a continuous manner. The central challenge is twofold: discovering and learning novel classes while mitigating the issue of catastrophic forgetting of established knowledge. To this end, we introduce a new paradigm called Adaptive Discovering and Merging (ADM) to discover novel categories adaptively in the incremental stage and integrate novel knowledge into the model without affecting the original knowledge. To discover novel classes adaptively, we decouple representation learning and novel class discovery, and use Triple Comparison (TC) and Probability Regularization (PR) to constrain the probability discrepancy and diversity for adaptive category assignment. To merge the learned novel knowledge adaptively, we propose a hybrid structure with base and novel branches named Adaptive Model Merging (AMM), which reduces the interference of the novel branch on the old classes to preserve the previous knowledge, and merges the novel branch to the base model without performance loss and parameter growth. Extensive experiments on several datasets show that ADM significantly outperforms existing class-incremental Novel Class Discovery (class-iNCD) approaches. Moreover, our AMM also benefits the class-incremental Learning (class-IL) task by alleviating the catastrophic forgetting problem. The source code is included in the supplementary materials.

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2

Dandliker, Peter J., Steve D. Pratt, Angela M. Nilius, Candace Black-Schaefer, Xiaoan Ruan, Danli L. Towne, Richard F. Clark, et al. "Novel Antibacterial Class." Antimicrobial Agents and Chemotherapy 47, no. 12 (December 2003): 3831–39. http://dx.doi.org/10.1128/aac.47.12.3831-3839.2003.

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ABSTRACT We report the discovery and characterization of a novel ribosome inhibitor (NRI) class that exhibits selective and broad-spectrum antibacterial activity. Compounds in this class inhibit growth of many gram-positive and gram-negative bacteria, including the common respiratory pathogens Streptococcus pneumoniae, Haemophilus influenzae, Staphylococcus aureus, and Moraxella catarrhalis, and are nontoxic to human cell lines. The first NRI was discovered in a high-throughput screen designed to identify inhibitors of cell-free translation in extracts from S. pneumoniae. The chemical structure of the NRI class is related to antibacterial quinolones, but, interestingly, the differences in structure are sufficient to completely alter the biochemical and intracellular mechanisms of action. Expression array studies and analysis of NRI-resistant mutants confirm this difference in intracellular mechanism and provide evidence that the NRIs inhibit bacterial protein synthesis by inhibiting ribosomes. Furthermore, compounds in the NRI series appear to inhibit bacterial ribosomes by a new mechanism, because NRI-resistant strains are not cross-resistant to other ribosome inhibitors, such as macrolides, chloramphenicol, tetracycline, aminoglycosides, or oxazolidinones. The NRIs are a promising new antibacterial class with activity against all major drug-resistant respiratory pathogens.

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3

Wang, Weishuai, Ting Lei, Qingchao Chen, and Yang Liu. "Semantic-Guided Novel Category Discovery." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (March 24, 2024): 5607–14. http://dx.doi.org/10.1609/aaai.v38i6.28371.

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The Novel Category Discovery problem aims to cluster an unlabeled set with the help of a labeled set consisting of disjoint but related classes. However, existing models treat class names as discrete one-hot labels and ignore the semantic understanding of these classes. In this paper, we propose a new setting named Semantic-guided Novel Category Discovery (SNCD), which requires the model to not only cluster the unlabeled images but also semantically recognize these images based on a set of their class names. The first challenge we confront pertains to effectively leveraging the class names of unlabeled images, given the inherent gap between the visual and linguistic domains. To address this issue, we incorporate a semantic-aware recognition mechanism. This is achieved by constructing dynamic class-wise visual prototypes as well as a semantic similarity matrix that enables the projection of visual features into the semantic space. The second challenge originates from the granularity disparity between the classification and clustering tasks. To deal with this, we develop a semantic-aware clustering process to facilitate the exchange of knowledge between the two tasks. Through extensive experiments, we demonstrate the mutual benefits of the recognition and clustering tasks, which can be jointly optimized. Experimental results on multiple datasets confirm the effectiveness of our proposed method. Our code is available at https://github.com/wang-weishuai/Semantic-guided-NCD.

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4

Shinozuka, Tsuyoshi, Shuichiro Ito, Takako Kimura, Masanori Izumi та Kenji Wakabayashi. "Discovery of a Novel Class of ERRα Agonists". ACS Medicinal Chemistry Letters 12, № 5 (21 квітня 2021): 817–21. http://dx.doi.org/10.1021/acsmedchemlett.1c00100.

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5

Cubitt, Jonathan, Mari Davies, Ross Riseley, Gabrielle Evans, Sian E. Gardiner, Benson M. Kariuki, Simon E. Ward, Emyr Lloyd-Evans, Helen Waller-Evans, and D. Heulyn Jones. "Beware of N-Benzoyloxybenzamides." Molecules 29, no. 21 (October 31, 2024): 5143. http://dx.doi.org/10.3390/molecules29215143.

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Following a High-Throughput Screening campaign to discover inhibitors of acid ceramidase, we report the novel and extremely potent covalent inhibitor, 1. Following resynthesis and stability monitoring, we discovered that 1 is chemically unstable and reacts with DMSO at room temperature. This mode of decomposition is likely general for this class of compound, and we urge caution for their use in drug discovery research.

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6

Feng, Juexiao, Yuhong Yang, Yanchun Xie, Yaqian Li, Yandong Guo, Yuchen Guo, Yuwei He, Liuyu Xiang, and Guiguang Ding. "Debiased Novel Category Discovering and Localization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (March 24, 2024): 1753–60. http://dx.doi.org/10.1609/aaai.v38i2.27943.

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In recent years, object detection in deep learning has experienced rapid development. However, most existing object detection models perform well only on closed-set datasets, ignoring a large number of potential objects whose categories are not defined in the training set. These objects are often identified as background or incorrectly classified as pre-defined categories by the detectors. In this paper, we focus on the challenging problem of Novel Class Discovery and Localization (NCDL), aiming to train detectors that can detect the categories present in the training data, while also actively discover, localize, and cluster new categories. We analyze existing NCDL methods and identify the core issue: object detectors tend to be biased towards seen objects, and this leads to the neglect of unseen targets. To address this issue, we first propose an Debiased Region Mining (DRM) approach that combines class-agnostic Region Proposal Network (RPN) and class-aware RPN in a complementary manner. Additionally, we suggest to improve the representation network through semi-supervised contrastive learning by leveraging unlabeled data. Finally, we adopt a simple and efficient mini-batch K-means clustering method for novel class discovery. We conduct extensive experiments on the NCDL benchmark, and the results demonstrate that the proposed DRM approach significantly outperforms previous methods, establishing a new state-of-the-art.

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7

Shearer, Barry G., Hari S. Patel, Andrew N. Billin, James M. Way, Deborah A. Winegar, Millard H. Lambert, Robert X. Xu та ін. "Discovery of a novel class of PPARδ partial agonists". Bioorganic & Medicinal Chemistry Letters 18, № 18 (вересень 2008): 5018–22. http://dx.doi.org/10.1016/j.bmcl.2008.08.011.

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8

Cheleski, Juliana, Josmar R. Rocha, Matheus P. Pinheiro, Helton José Wiggers, Albérico B. F. da Silva, Maria C. Nonato, and Carlos A. Montanari. "Novel insights for dihydroorotate dehydrogenase class 1A inhibitors discovery." European Journal of Medicinal Chemistry 45, no. 12 (December 2010): 5899–909. http://dx.doi.org/10.1016/j.ejmech.2010.09.055.

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9

Fukunaga, Alex S. "Automated Discovery of Local Search Heuristics for Satisfiability Testing." Evolutionary Computation 16, no. 1 (March 2008): 31–61. http://dx.doi.org/10.1162/evco.2008.16.1.31.

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The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.

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10

Hilgeroth, Andreas, Marc Hemmer, Sebastian Neuber, Josef Molnar, and Hermann Lage. "Discovery of 9,10-Dihydroacridines as Novel Class of ABCB1 Inhibitors." Medicinal Chemistry 11, no. 4 (April 29, 2015): 329–35. http://dx.doi.org/10.2174/1573406410666141111100720.

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11

Ward, K. M., S. T. Hubbard, S. B. Jones, D. M. Nason, M. H. Lee, C. K. Biggers, J. Nowakowski, et al. "Discovery of a novel, structurally unique class of muscarinic agonists." Life Sciences 60, no. 13-14 (February 1997): 1164. http://dx.doi.org/10.1016/s0024-3205(97)84300-7.

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12

Shen, Wenbin, Xinhua Lu, Jingtong Zhu, Yunlong Mu, Yan Xu, Jian Gao, Xuexia Zhang, and Zhihui Zheng. "Discovery of naphthacemycins as a novel class of PARP1 inhibitors." Bioorganic & Medicinal Chemistry Letters 29, no. 15 (August 2019): 1904–8. http://dx.doi.org/10.1016/j.bmcl.2019.05.055.

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13

Ohwada, J., H. Ebiike, H. Kawada, M. Tsukazaki, M. Nakamura, K. Morikami, K. Morita, M. Yoshida, O. Kondoh, and N. Shimma. "108 Discovery of CH5132799, a novel class I PI3K inhibitor." European Journal of Cancer Supplements 8, no. 7 (November 2010): 41. http://dx.doi.org/10.1016/s1359-6349(10)71813-9.

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14

Brayman, Timothy G., and John W. Wilks. "Sensitive Assay for Antifungal Activity of Glucan Synthase Inhibitors That Uses Germ Tube Formation in Candida albicans as an End Point." Antimicrobial Agents and Chemotherapy 47, no. 10 (October 2003): 3305–10. http://dx.doi.org/10.1128/aac.47.10.3305-3310.2003.

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ABSTRACT We implemented a simple, sensitive, objective, and rapid cellular assay to reveal the antifungal activity of a novel class of glucan synthase inhibitors. The assay, especially useful for early drug discovery, measures the transformation of Candida albicans from the yeast form to the hyphal form. Test compounds were ranked by potency (50% inhibitory concentration) and efficacy (percent inhibition of germ tube formation); the intra-assay coefficients of variation for these parameters were 17 and 5%, respectively. The germ tube formation assay proved useful for the early-stage antifungal characterization of a novel class of glucan synthase inhibitors discovered at Pharmacia. Drug concentrations required in this assay to inhibit germ tube formation were lower for 90% of the novel compounds than the concentrations required to determine MICs. The method may have utility for other mechanistic classes of antifungal compounds during the hit-to-lead transition of drug discovery.

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15

Yu, Qing, Daiki Ikami, Go Irie, and Kiyoharu Aizawa. "Self-Labeling Framework for Novel Category Discovery over Domains." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3161–69. http://dx.doi.org/10.1609/aaai.v36i3.20224.

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Unsupervised domain adaptation (UDA) has been highly successful in transferring knowledge acquired from a label-rich source domain to a label-scarce target domain. Open-set domain adaptation (open-set DA) and universal domain adaptation (UniDA) have been proposed as solutions to the problem concerning the presence of additional novel categories in the target domain. Existing open-set DA and UniDA approaches treat all novel categories as one unified unknown class and attempt to detect this unknown class during the training process. However, the features of the novel categories learned by these methods are not discriminative. This limits the applicability of UDA in the further classification of these novel categories into their original categories, rather than assigning them to a single unified class. In this paper, we propose a self-labeling framework to cluster all target samples, including those in the ''unknown'' categories. We train the network to learn the representations of target samples via self-supervised learning (SSL) and to identify the seen and unseen (novel) target-sample categories simultaneously by maximizing the mutual information between labels and input data. We evaluated our approach under different DA settings and concluded that our method generally outperformed existing ones by a wide margin.

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16

Bossio, Ricardo, Stefano Marcaccini, and Roberto Pepino. "A novel class of nitrile ylide." Tetrahedron Letters 27, no. 38 (1986): 4643–46. http://dx.doi.org/10.1016/s0040-4039(00)85027-3.

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17

Zhou, Jiaying, Yang Liu, and Qingchao Chen. "Novel Class Discovery in Chest X-rays via Paired Images and Text." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (March 24, 2024): 7650–58. http://dx.doi.org/10.1609/aaai.v38i7.28598.

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Novel class discover(NCD) aims to identify new classes undefined during model training phase with the help of knowledge of known classes. Many methods have been proposed and notably boosted performance of NCD in natural images. However, there has been no work done in discovering new classes based on medical images and disease categories, which is crucial for understanding and diagnosing specific diseases. Moreover, most of the existing methods only utilize information from image modality and use labels as the only supervisory information. In this paper, we propose a multi-modal novel class discovery method based on paired images and text, inspired by the low classification accuracy of chest X-ray images and the relatively higher accuracy of the paired text. Specifically, we first pretrain the image encoder and text encoder with multi-modal contrastive learning on the entire dataset and then we generate pseudo-labels separately on the image branch and text branch. We utilize intra-modal consistency to assess the quality of pseudo-labels and adjust the weights of the pseudo-labels from both branches to generate the ultimate pseudo-labels for training. Experiments on eight subset splits of MIMIC-CXR-JPG dataset show that our method improves the clustering performance of unlabeled classes by about 10% on average compared to state-of-the-art methods. Code is available at: https://github.com/zzzzzzzzjy/MMNCD-main.

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18

Glock, Jutta, James Allen, Thierry Niderman, Hans Ulrich Haas, Renold Chollet, Martin Eberle, Peter Renold, et al. "Lead-Discovery of bis-Aromatic Alkynes: A Novel Class of Herbicides." CHIMIA International Journal for Chemistry 62, no. 1 (February 27, 2008): 23–28. http://dx.doi.org/10.2533/chimia.2008.23.

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19

Khanna, May, Che-Hong Chen, Ann Kimble-Hill, Bibek Parajuli, Samantha Perez-Miller, Sulochanadevi Baskaran, Jeewon Kim, et al. "Discovery of a Novel Class of Covalent Inhibitor for Aldehyde Dehydrogenases." Journal of Biological Chemistry 286, no. 50 (October 21, 2011): 43486–94. http://dx.doi.org/10.1074/jbc.m111.293597.

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20

Mahadevan, D., G. Powis, E. A. Mash, B. George, V. M. Gokhale, S. Zhang, K. Shakalya, et al. "Discovery of a novel class of AKT pleckstrin homology domain inhibitors." Molecular Cancer Therapeutics 7, no. 9 (September 1, 2008): 2621–32. http://dx.doi.org/10.1158/1535-7163.mct-07-2276.

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21

Foloppe, Nicolas, Nicola H. Allen, Carol H. Bentley, Teresa D. Brooks, Guy Kennett, Antony R. Knight, Stefania Leonardi, Anil Misra, Nathaniel J. T. Monck, and Daniel M. Sellwood. "Discovery of a novel class of selective human CB1 inverse agonists." Bioorganic & Medicinal Chemistry Letters 18, no. 3 (February 2008): 1199–206. http://dx.doi.org/10.1016/j.bmcl.2007.11.133.

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22

Nie, Feiping, Shiming Xiang, Yun Liu, and Changshui Zhang. "A general graph-based semi-supervised learning with novel class discovery." Neural Computing and Applications 19, no. 4 (September 11, 2009): 549–55. http://dx.doi.org/10.1007/s00521-009-0305-8.

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23

Winston-McPherson, Gabrielle N., Haibo Xie, Ka Yang, Xiaoxun Li, Dongxu Shu, and Weiping Tang. "Discovery of 2,3′-diindolylmethanes as a novel class of PCSK9 modulators." Bioorganic & Medicinal Chemistry Letters 29, no. 16 (August 2019): 2345–48. http://dx.doi.org/10.1016/j.bmcl.2019.06.014.

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24

Hemmer, Marc, Sören Krawczyk, Ina Simon, Hermann Lage, and Andreas Hilgeroth. "Discovery of substituted 1,4-dihydroquinolines as novel class of ABCB1 modulators." Bioorganic & Medicinal Chemistry 23, no. 15 (August 2015): 5015–21. http://dx.doi.org/10.1016/j.bmc.2015.05.016.

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25

Hirsch, Kenneth S., C. David Jones, Terry D. Lindstrom, Nancy B. Stamm, Gregory P. Sutton, Harold M. Taylor, and Dix E. Weaver. "Discovery and development of a novel class of nonsteroidal aromatase inhibitors." Steroids 50, no. 1-3 (July 1987): 201–17. http://dx.doi.org/10.1016/0039-128x(83)90072-7.

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26

Zhou, Wengang, and Julie A. Dickerson. "A novel class dependent feature selection method for cancer biomarker discovery." Computers in Biology and Medicine 47 (April 2014): 66–75. http://dx.doi.org/10.1016/j.compbiomed.2014.01.014.

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27

Lopat’eva, Elena R., Alexander S. Budnikov, Igor B. Krylov, Anna L. Alekseenko, Alexey I. Ilovaisky, Alexey P. Glinushkin, and Alexander O. Terent’ev. "4-Disubstituted Pyrazolin-3-Ones—Novel Class of Fungicides against Phytopathogenic Fungi." Agrochemicals 2, no. 1 (January 9, 2023): 34–46. http://dx.doi.org/10.3390/agrochemicals2010004.

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The search for fungicides of novel classes is the long-standing priority in crop protection due to the continuous development of fungal resistance against currently used types of active compounds. Recently, 4-nitropyrazolin-3-ones were discovered as highly potent fungicides, of which activity was believed to be strongly associated with the presence of a nitro group in the pyrazolone ring. In this paper, a series of 4-substituted pyrazolin-3-ones were synthesized and their fungicidal activity against an important species of phytopathogenic fungi (Venturia inaequalis, Rhizoctonia solani, Fusarium oxysporum, Fusarium moniliforme, Bipolaris sorokiniana, and Sclerotinia sclerotiorum) was tested in vitro. We discovered that 4-mono and 4,4-dihalogenated pyrazolin-3-ones demonstrate fungicidal activity comparable to that of 4-nitropyrazolin-3-ones and other modern fungicides (such as kresoxim methyl). This discovery indicates that NO2 moiety can be replaced by other groups of comparable size and electronic properties without the loss of fungicidal activity and significantly expands the scope of potent new fungicides based on a pyrazolin-3-one fragment.

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28

Kundargi, Shivanand, Tejas Anvekar, Ramesh Tabib, and Uma Mudenagudi. "Novel Class Discovery for Representation of Real-World Heritage Data as Neural Radiance Fields (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23552–53. http://dx.doi.org/10.1609/aaai.v38i21.30469.

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Neural Radiance Fields (NeRF) have been extensively explored as a leading approach for modeling and representing 3D data across various domains. Their ability to capture arbitrary scale point clouds and generate novel views makes them particularly valuable for digitizing cultural heritage sites. However, despite their impressive rendering capabilities, prior methods have often overlooked a significant real-world challenge: handling open-world scenarios characterized by unstructured data containing multiple classes in a single set of unlabeled images. To address this challenge, we propose a novel method NCD-NeRF that leverages Novel-Class Discovery to effectively tackle the complexities inherent in real-world data with unlabeled classes while excelling in producing high-quality NeRF representation. To validate our approach, we conducted a benchmarking analysis using a custom-collected dataset featuring UNESCO World Heritage sites in India. We observe that our proposed NCD-NeRF can parallely discover novel classes and render high-quality 3D volumes.

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29

Rodriguez, David, Silvia Kanzler, Jan Zorgdrager, Sebastian Halkes, Jan van de Velde, and Wolfgang Reischl. "A Novel Class of Aromatic Vitamin D Analogs." Current Pharmaceutical Design 6, no. 7 (May 1, 2000): 749–54. http://dx.doi.org/10.2174/1381612003400344.

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30

Pasche, Nadine, and Dario Neri. "Immunocytokines: a novel class of potent armed antibodies." Drug Discovery Today 17, no. 11-12 (June 2012): 583–90. http://dx.doi.org/10.1016/j.drudis.2012.01.007.

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31

Moloney, Mark, Paul Trippier, Muhammad Yaqoob, and Zhaoyang Wang. "The Oxazolomycins: A Structurally Novel Class of Bioactive Compounds." Current Drug Discovery Technologies 1, no. 3 (October 1, 2004): 181–99. http://dx.doi.org/10.2174/1570163043334974.

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32

Rodionov, Dmitry A., Peter Hebbeln, Aymerick Eudes, Josy ter Beek, Irina A. Rodionova, Guus B. Erkens, Dirk J. Slotboom, et al. "A Novel Class of Modular Transporters for Vitamins in Prokaryotes." Journal of Bacteriology 191, no. 1 (October 17, 2008): 42–51. http://dx.doi.org/10.1128/jb.01208-08.

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ABSTRACT The specific and tightly controlled transport of numerous nutrients and metabolites across cellular membranes is crucial to all forms of life. However, many of the transporter proteins involved have yet to be identified, including the vitamin transporters in various human pathogens, whose growth depends strictly on vitamin uptake. Comparative analysis of the ever-growing collection of microbial genomes coupled with experimental validation enables the discovery of such transporters. Here, we used this approach to discover an abundant class of vitamin transporters in prokaryotes with an unprecedented architecture. These transporters have energy-coupling modules comprised of a conserved transmembrane protein and two nucleotide binding proteins similar to those of ATP binding cassette (ABC) transporters, but unlike ABC transporters, they use small integral membrane proteins to capture specific substrates. We identified 21 families of these substrate capture proteins, each with a different specificity predicted by genome context analyses. Roughly half of the substrate capture proteins (335 cases) have a dedicated energizing module, but in 459 cases distributed among almost 100 gram-positive bacteria, including numerous human pathogens, different and unrelated substrate capture proteins share the same energy-coupling module. The shared use of energy-coupling modules was experimentally confirmed for folate, thiamine, and riboflavin transporters. We propose the name energy-coupling factor transporters for the new class of membrane transporters.

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33

CS, Sharanya. "Fermentation of Polyherbal Preparations as in Ayurveda: a Novel Protocol for Drug-Lead Discovery." Journal of Natural & Ayurvedic Medicine 3, no. 3 (July 15, 2019): 1–8. http://dx.doi.org/10.23880/jonam-16000197.

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Анотація:

Background: Alcoholic fermentation of polyherbal preparations to yield medicinal wines has been in vogue since the dawn of Ayurveda. It was not explicitly stated in the classics of Ayurveda, why to ferment medicines? However, this unique class of medicines was considered to be providing better/higher active medicinal principles than their unfermented forms. There are only a few reports expressing evidences from investigations driven by this hypothesis. Berberine and its derivatives form such a model for assessing the positive changes brought about by Ayurvedic fermentation, as reported earlier. Materials and methods: Plants were collected and polyherbal formulations were prepared as per the classic Ayurvedic texts and the formulation was compared with whole plant extract through LC/MS-MS for the production of their derivatives. Results: We were austerely analysing for the derivatives of piperine during the formulation production. In our analysis it is proven that piperine and its derivatives were found in the herbal formulation which leads to another model for assessing the positive changes due to Ayurvedic fermentation. Conclusion: The significance of looking at Ayurvedic fermentations with novel interests as a drug discovery protocol is suggested, since it might provide new, more potent, water soluble and biocompatible drug-leads. It may be used in conjunction with high throughput systems and would deliver drug-leads present naturally

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Smith, Emma E., Jennifer N. McClean, Leonie A. Cooke, Jean-Louis Duprey, Maighréad McCourt, Martin M. Fabani, James H. R. Tucker, and Joseph S. Vyle. "A novel structural class of photoswitchable oligonucleotide." Tetrahedron Letters 48, no. 37 (September 2007): 6569–72. http://dx.doi.org/10.1016/j.tetlet.2007.07.028.

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35

Badolato, Mariateresa, Fabrizio Manetti, Antonio Garofalo, and Francesca Aiello. "Triazolopyrimidinium salts: discovery of a new class of agents for cancer therapy." Future Medicinal Chemistry 12, no. 5 (March 2020): 387–402. http://dx.doi.org/10.4155/fmc-2019-0317.

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Aim: The [1,2,4]triazolo[1,5- a]pyrimidine core is highly privileged in medicinal chemistry due to its versatile pharmacological activity profile. Recently, the search for novel anticancer agents has focused on [1,2,4]triazolo[1,5- a]pyrimidine derivatives. Results: Our hit functionalization has led to the discovery of new [1,2,4]triazolo[1,5- a]pyrimidinium salts with potential anticancer activity. Among a small library of molecules, compound 9 significantly inhibits cancer cell growth in a panel of in vitro models. Molecular docking studies and preliminary binding assay have displayed that 9 could directly bind the Src homology 2 (SH2) domain of STAT3 protein. Conclusion: Compound 9 is a novel promising lead compound that motivates additional evaluation of [1,2,4]triazolo[1,5- a]pyrimidinium salts as novel potential chemotherapeutics.

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Xu, Zi Fei, Sheng Tao Bo, Mei Jing Wang, Jing Shi, Rui Hua Jiao, Yang Sun, Qiang Xu, Ren Xiang Tan, and Hui Ming Ge. "Discovery and biosynthesis of bosamycins from Streptomyces sp. 120454." Chemical Science 11, no. 34 (2020): 9237–45. http://dx.doi.org/10.1039/d0sc03469j.

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Ostash, Bohdan. "Recent Advances in Functions and Biotechnological Potential of Pleiotropic Transcriptional Factor AdpA." Current Biotechnology 13, no. 3 (September 2024): 131–39. http://dx.doi.org/10.2174/0122115501322358240824115255.

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The specialized metabolism of the members of class Actinomycetes served as one of the deepest sources of compounds for the pharmaceutical industry. Within this class species of genus Streptomyces stand out as the most diverse and prolific producers of novel scaffolds. At some point at the end of the 20th century, chemical-microbiological screening of actinomycetes seemed to largely sample their specialized metabolism chemical space. Contrary to traditional discovery methods that directly focus on the molecule or its bioactivity, the availability of sequenced actinomycete genomes opens the door for novel biosynthetic gene clusters (BGC) for specialized metabolism. The genome-based approaches reveal the striking richness and diversity of BGCs, to which the “pre-genome” discovery paradigm was myopic. In most cases, small molecules encoded within these BGCs remain unknown, and finding efficient ways to probe such unexplored BGCs becomes one of the pressing issues of current biotechnology. Here, the focus is on the biology of pleiotropic transcriptional factor (TF) AdpA, whose gene is invariably present in Streptomyces genomes. The review will portray how this TF impacts the morphogenesis and metabolism of Streptomyces and how it can be exploited to discover novel natural products.

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38

Chouha, Nora, Hassan Hammoud, Simone Brogi, Giuseppe Campiani, Caroline Welsch, Caroline Robert, Stéphan Vagner, Thierry Cresteil, Embarek Bentouhami, and Laurent Désaubry. "Discovery of Iminobenzimidazole Derivatives as Novel Cytotoxic Agents." Open Medicinal Chemistry Journal 12, no. 1 (August 31, 2018): 74–83. http://dx.doi.org/10.2174/1874104501812010074.

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In our quest to identify inhibitors of the eukaryotic translation initiation factor 4F (eIF4F), we serendipitously discovered a novel cytotoxic agent. Even though this compound did not inhibit translation, we explored the structural requirements for its cytotoxicity due to its structural originality. A series of 1,3-disubstituted iminobenzimidazoles was synthesized and evaluated for their in vitro cytotoxicity. The structure-activity relationship studies demonstrate that hydrophobic substituent is essential for activity. The most active compounds displayed a cytotoxicity in KB, HL60 and HCT116 human cancer cells with an IC50 of about 1μM. These first-in-class series of low molecular weight synthetic molecules may provide the basis for the development of new anticancer drugs.

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Merwin, Nishanth J., Walaa K. Mousa, Chris A. Dejong, Michael A. Skinnider, Michael J. Cannon, Haoxin Li, Keshav Dial, Mathusan Gunabalasingam, Chad Johnston, and Nathan A. Magarvey. "DeepRiPP integrates multiomics data to automate discovery of novel ribosomally synthesized natural products." Proceedings of the National Academy of Sciences 117, no. 1 (December 23, 2019): 371–80. http://dx.doi.org/10.1073/pnas.1901493116.

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Microbial natural products represent a rich resource of evolved chemistry that forms the basis for the majority of pharmacotherapeutics. Ribosomally synthesized and posttranslationally modified peptides (RiPPs) are a particularly interesting class of natural products noted for their unique mode of biosynthesis and biological activities. Analyses of sequenced microbial genomes have revealed an enormous number of biosynthetic loci encoding RiPPs but whose products remain cryptic. In parallel, analyses of bacterial metabolomes typically assign chemical structures to only a minority of detected metabolites. Aligning these 2 disparate sources of data could provide a comprehensive strategy for natural product discovery. Here we present DeepRiPP, an integrated genomic and metabolomic platform that employs machine learning to automate the selective discovery and isolation of novel RiPPs. DeepRiPP includes 3 modules. The first, NLPPrecursor, identifies RiPPs independent of genomic context and neighboring biosynthetic genes. The second module, BARLEY, prioritizes loci that encode novel compounds, while the third, CLAMS, automates the isolation of their corresponding products from complex bacterial extracts. DeepRiPP pinpoints target metabolites using large-scale comparative metabolomics analysis across a database of 10,498 extracts generated from 463 strains. We apply the DeepRiPP platform to expand the landscape of novel RiPPs encoded within sequenced genomes and to discover 3 novel RiPPs, whose structures are exactly as predicted by our platform. By building on advances in machine learning technologies, DeepRiPP integrates genomic and metabolomic data to guide the isolation of novel RiPPs in an automated manner.

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40

El-Awady, Raafat, Ekram Saleh, Rifat Hamoudi, Wafaa S. Ramadan, Ralph Mazitschek, Manal A. Nael, Khaled M. Elokely, et al. "Discovery of novel class of histone deacetylase inhibitors as potential anticancer agents." Bioorganic & Medicinal Chemistry 42 (July 2021): 116251. http://dx.doi.org/10.1016/j.bmc.2021.116251.

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41

Payne, David J., William H. Miller, Valerie Berry, John Brosky, Walter J. Burgess, Emile Chen, Walter E. DeWolf, et al. "Discovery of a Novel and Potent Class of FabI-Directed Antibacterial Agents." Antimicrobial Agents and Chemotherapy 46, no. 10 (October 2002): 3118–24. http://dx.doi.org/10.1128/aac.46.10.3118-3124.2002.

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ABSTRACT Bacterial enoyl-acyl carrier protein (ACP) reductase (FabI) catalyzes the final step in each elongation cycle of bacterial fatty acid biosynthesis and is an attractive target for the development of new antibacterial agents. High-throughput screening of the Staphylococcus aureus FabI enzyme identified a novel, weak inhibitor with no detectable antibacterial activity against S. aureus. Iterative medicinal chemistry and X-ray crystal structure-based design led to the identification of compound 4 [(E)-N-methyl-N-(2-methyl-1H-indol-3-ylmethyl)-3-(7-oxo-5,6,7,8-tetrahydro-1,8-naphthyridin-3-yl)acrylamide], which is 350-fold more potent than the original lead compound obtained by high-throughput screening in the FabI inhibition assay. Compound 4 has exquisite antistaphylococci activity, achieving MICs at which 90% of isolates are inhibited more than 500 times lower than those of nine currently available antibiotics against a panel of multidrug-resistant strains of S. aureus and Staphylococcus epidermidis. Furthermore, compound 4 exhibits excellent in vivo efficacy in an S. aureus infection model in rats. Biochemical and genetic approaches have confirmed that the mode of antibacterial action of compound 4 and related compounds is via inhibition of FabI. Compound 4 also exhibits weak FabK inhibitory activity, which may explain its antibacterial activity against Streptococcus pneumoniae and Enterococcus faecalis, which depend on FabK and both FabK and FabI, respectively, for their enoyl-ACP reductase function. These results show that compound 4 is representative of a new, totally synthetic series of antibacterial agents that has the potential to provide novel alternatives for the treatment of S. aureus infections that are resistant to our present armory of antibiotics.

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42

Shao, Pengcheng P., Feng Ye, Ann E. Weber, Xiaohua Li, Kathryn A. Lyons, William H. Parsons, Maria L. Garcia, et al. "Discovery of a novel class of isoxazoline voltage gated sodium channel blockers." Bioorganic & Medicinal Chemistry Letters 19, no. 18 (September 2009): 5329–33. http://dx.doi.org/10.1016/j.bmcl.2009.07.125.

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43

Montgomery, Justin I., James F. Smith, Andrew P. Tomaras, Richard Zaniewski, Craig J. McPherson, Laura A. McAllister, Sandra Hartman-Neumann, et al. "Discovery and characterization of a novel class of pyrazolopyrimidinedione tRNA synthesis inhibitors." Journal of Antibiotics 68, no. 6 (December 3, 2014): 361–67. http://dx.doi.org/10.1038/ja.2014.163.

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44

Imbriglio, Jason E., Dong-Ming Shen, Rui Liang, Ken Marby, Ming You, Hye Won Youm, Zhe Feng, et al. "Discovery and Pharmacology of a Novel Class of Diacylglycerol Acyltransferase 2 Inhibitors." Journal of Medicinal Chemistry 58, no. 23 (November 20, 2015): 9345–53. http://dx.doi.org/10.1021/acs.jmedchem.5b01345.

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45

Brand, Stephen, Laura A. T. Cleghorn, Stuart P. McElroy, David A. Robinson, Victoria C. Smith, Irene Hallyburton, Justin R. Harrison, et al. "Discovery of a Novel Class of Orally Active Trypanocidal N-Myristoyltransferase Inhibitors." Journal of Medicinal Chemistry 55, no. 1 (December 7, 2011): 140–52. http://dx.doi.org/10.1021/jm201091t.

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46

Ohwada, Jun, Hirosato Ebiike, Hatsuo Kawada, Masao Tsukazaki, Mitsuaki Nakamura, Takuya Miyazaki, Kenji Morikami, et al. "Discovery and biological activity of a novel class I PI3K inhibitor, CH5132799." Bioorganic & Medicinal Chemistry Letters 21, no. 6 (March 2011): 1767–72. http://dx.doi.org/10.1016/j.bmcl.2011.01.065.

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47

Lu, Dai, Zhaoxing Meng, Ganesh A. Thakur, Pusheng Fan, John Steed, Cindy L. Tartal, Dow P. Hurst, et al. "Adamantyl Cannabinoids: A Novel Class of Cannabinergic Ligands." Journal of Medicinal Chemistry 48, no. 14 (July 2005): 4576–85. http://dx.doi.org/10.1021/jm058175c.

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48

Hinman, Mira M., Teresa A. Rosenberg, Darlene Balli, Candace Black-Schaefer, Linda E. Chovan, Douglas Kalvin, Philip J. Merta, et al. "Novel Antibacterial Class: A Series of Tetracyclic Derivatives." Journal of Medicinal Chemistry 49, no. 16 (August 2006): 4842–56. http://dx.doi.org/10.1021/jm060010w.

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49

Mohacsi, Erno, Jay O'Brien, John Blount, and Jerry Sepinwall. "Acylmorphinans. A novel class of potent analgesic agents." Journal of Medicinal Chemistry 28, no. 9 (September 1985): 1177–80. http://dx.doi.org/10.1021/jm00147a009.

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50

González-Rosende, M. Eugenia, Teresa Olivar, Encarna Castillo, and José Sepúlveda-Arques. "2-Sulfonyliminodihydropyrimidines: A Novel Class of Analgesic Compounds." Archiv der Pharmazie 341, no. 11 (November 2008): 690–95. http://dx.doi.org/10.1002/ardp.200800107.

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