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Journal articles on the topic 'Ligand based virtual screening'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Kato, Koya, and George Chikenji. "1P266 Development of Ligand Based Virtual Screening considering protein-ligand interaction(22A. Bioinformatics: Structural genomics,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S150. http://dx.doi.org/10.2142/biophys.53.s150_1.

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2

Douguet, Dominique. "Ligand-Based Approaches in Virtual Screening." Current Computer Aided-Drug Design 4, no. 3 (September 1, 2008): 180–90. http://dx.doi.org/10.2174/157340908785747456.

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3

Stahura, Florence, and Jürgen Bajorath. "New Methodologies for Ligand-Based Virtual Screening." Current Pharmaceutical Design 11, no. 9 (April 1, 2005): 1189–202. http://dx.doi.org/10.2174/1381612053507549.

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4

Ahmed, Ali, Naomie Salim, and Ammar Abdo. "Fragment Reweighting in Ligand-Based Virtual Screening." Advanced Science Letters 19, no. 9 (September 1, 2013): 2782–86. http://dx.doi.org/10.1166/asl.2013.5012.

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5

HIRAYAMA, Noriaki. "Virtual Screening Based on Protein-Ligand Interactions." YAKUGAKU ZASSHI 127, no. 1 (January 1, 2007): 101–2. http://dx.doi.org/10.1248/yakushi.127.101.

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6

Jain, Ajay N. "Ligand-Based Structural Hypotheses for Virtual Screening." Journal of Medicinal Chemistry 47, no. 4 (February 2004): 947–61. http://dx.doi.org/10.1021/jm030520f.

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7

Abdo, Ammar, Beining Chen, Christoph Mueller, Naomie Salim, and Peter Willett. "Ligand-Based Virtual Screening Using Bayesian Networks." Journal of Chemical Information and Modeling 50, no. 6 (May 26, 2010): 1012–20. http://dx.doi.org/10.1021/ci100090p.

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8

Dai, Weixing, and Dianjing Guo. "A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability." Molecules 24, no. 13 (June 30, 2019): 2414. http://dx.doi.org/10.3390/molecules24132414.

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Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical characteristic of ligands are high-dimensional. We here describe a machine learning algorithm LBS (local beta screening) for ligand-based virtual screening. The unique characteristic of LBS is that it quantifies the generalization ability of screening directly by a refined loss function, and thus can assess the risk of over-fitting accurately and efficiently for imbalanced and high-dimensional data in ligand-based virtual screening without the help of resampling methods such as cross validation. The robustness of LBS was demonstrated by a simulation study and tests on real datasets, in which LBS outperformed conventional algorithms in terms of screening accuracy and model interpretation. LBS was then used for screening potential activators of HIV-1 integrase multimerization in an independent compound library, and the virtual screening result was experimentally validated. Of the 25 compounds tested, six were proved to be active. The most potent compound in experimental validation showed an EC50 value of 0.71 µM.

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9

Kato, Koya, and George Chikenji. "2P272 A Ligand Based Virtual Screening method that takes into account of protein-ligand interactions(22A. Bioinformatics:Structural genomics,Poster)." Seibutsu Butsuri 54, supplement1-2 (2014): S240. http://dx.doi.org/10.2142/biophys.54.s240_2.

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10

Rayevsky, O. V., O. M. Demchyk, P. A. Karpov, S. P. Ozheredov, S. I. Spivak, A. I. Yemets, and Ya B. Blume. "Structure-based virtual screening for new lead compounds targeted Plasmodium α-tubulin." Faktori eksperimental'noi evolucii organizmiv 28 (August 31, 2021): 135–39. http://dx.doi.org/10.7124/feeo.v28.1389.

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Aim. Search for new dinitroaniline and phosphorothioamide compounds, capable of selective binding with Plasmodium α-tubulin, affecting its mitotic apparatus. Methods. Structural biology methods of computational prediction of protein-ligand interaction: molecular docking, molecular dynamics and pharmacophore analysis. Selection of compounds based on pharmacophore characteristics and virtual screening results. Results. The protocol and required structural conditions for target (α-tubulin of P. falciparum) preparation and correct modeling of the ligand-protein interaction (docking and virtual screening) were developed. The generalized pharmacophore model of ligand-protein interaction and key functional groups of ligands responsible for specific binding were identified. Conclusions. Based on results of virtual screening, 22 commercial compounds were selected. Identified compounds proposed as potential inhibitors of Plasmodium mitotic machinery and the base of new antimalarial drugs. Keywords: malaria, Plasmodium, intermolecular interaction, dinitroaniline derived, phosphorothioamidate derived.

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11

Abdo, Ammar, Faisal Saeed, Hentabli Hamza, Ali Ahmed, and Naomie Salim. "Ligand expansion in ligand-based virtual screening using relevance feedback." Journal of Computer-Aided Molecular Design 26, no. 3 (January 17, 2012): 279–87. http://dx.doi.org/10.1007/s10822-012-9543-4.

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12

Vasanthanathan, Poongavanam, Jeroen Lastdrager, Chris Oostenbrink, Jan N. M. Commandeur, Nico P. E. Vermeulen, Flemming S. Jørgensen, and Lars Olsen. "Identification of CYP1A2 ligands by structure-based and ligand-based virtual screening." MedChemComm 2, no. 9 (2011): 853. http://dx.doi.org/10.1039/c1md00087j.

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13

Tao, Wei Ye, Lai You Wang, Guo Quan Huang, and Man Luo. "Preparation of Target CETP in Docking-Based Virtual Screening." Applied Mechanics and Materials 477-478 (December 2013): 1495–98. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.1495.

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When conducting docking-based virtual screening, we should carefully prepare the target protein. Generally, the ligands coupled with the receptor-ligand complex are often deleted from the crystal structure, but it is unknown that in which situation the ligands should be deleted. Taking CETP for example, this study conducted virtual screening against CETP through 2 different styles. In style 1, the cholesteryl ester near the active site was deleted. In style 2, the cholesteryl ester was kept to conduct the virtual screening. We found that the results were very different from each other and style 2 was the preferable choice in this situation. The reason why like this is that there is strong repulsion between drug molecule and the cholesteryl ester.

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14

Villoutreix, Bruno, Richard Eudes, and Maria Miteva. "Structure-Based Virtual Ligand Screening: Recent Success Stories." Combinatorial Chemistry & High Throughput Screening 12, no. 10 (December 1, 2009): 1000–1016. http://dx.doi.org/10.2174/138620709789824682.

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15

Jayaraj, P. B., S. Sanjay, Koustub Raja, G. Gopakumar, and U. C. Jaleel. "Ligand Based Virtual Screening Using Self-organizing Maps." Protein Journal 41, no. 1 (January 13, 2022): 44–54. http://dx.doi.org/10.1007/s10930-021-10030-9.

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16

Nair, Viswajit Vinod, Sonaal Pathlai Pradeep, Vaishnavi Sudheer Nair, P. N. Pournami, G. Gopakumar, and P. B. Jayaraj. "Deep Sequence Models for Ligand-Based Virtual Screening." Journal of Computational Biophysics and Chemistry 21, no. 02 (February 4, 2022): 207–17. http://dx.doi.org/10.1142/s2737416522500107.

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The past few years have witnessed machine learning techniques take the limelight in multiple research domains. One such domain that has reaped the benefits of machine learning is computer-aided drug discovery, where the search space for candidate drug molecules is decreased using methods such as virtual screening. Current state-of-the-art sequential neural network models have shown promising results and we would like to replicate similar results with virtual screening using the encoded molecular information known as simplified molecular-input line-entry system (SMILES). Our work includes the use of attention-based sequential models — the long short-term memory with attention and an optimized version of the transformer network specifically designed to deal with SMILES (ChemBERTa). We also propose the “Overall Screening Efficacy”, an averaging metric that aggregates and encapsulates the model performance over multiple datasets. We found an overall improvement of about [Formula: see text] over the benchmark model, which relied on parallelized random forests.

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17

Ewing, Todd, J. Christian Baber, and Miklos Feher. "Novel 2D Fingerprints for Ligand-Based Virtual Screening." Journal of Chemical Information and Modeling 46, no. 6 (September 16, 2006): 2423–31. http://dx.doi.org/10.1021/ci060155b.

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18

Willett, Peter. "FUSING SIMILARITY RANKINGS IN LIGAND-BASED VIRTUAL SCREENING." Computational and Structural Biotechnology Journal 5, no. 6 (February 2013): e201302002. http://dx.doi.org/10.5936/csbj.201302002.

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19

Chen, Beining, Robert F. Harrison, Jérôme Hert, Chido Mpanhanga, Peter Willett, and David J. Wilton. "Ligand-based virtual screening using binary kernel discrimination." Molecular Simulation 31, no. 8 (July 2005): 597–604. http://dx.doi.org/10.1080/08927020500134177.

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20

Jayaraj, P. B., and Samyak Jain. "Ligand based virtual screening using SVM on GPU." Computational Biology and Chemistry 83 (December 2019): 107143. http://dx.doi.org/10.1016/j.compbiolchem.2019.107143.

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21

von Behren, Mathias M., and Matthias Rarey. "Ligand-based virtual screening under partial shape constraints." Journal of Computer-Aided Molecular Design 31, no. 4 (March 18, 2017): 335–47. http://dx.doi.org/10.1007/s10822-017-0011-z.

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22

Zavodszky, Maria I., Anjali Rohatgi, Jeffrey R. Van Voorst, Honggao Yan, and Leslie A. Kuhn. "Scoring ligand similarity in structure-based virtual screening." Journal of Molecular Recognition 22, no. 4 (February 20, 2009): 280–92. http://dx.doi.org/10.1002/jmr.942.

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23

Guzelj, Samo, Tihomir Tomašič, and Žiga Jakopin. "Novel Scaffolds for Modulation of NOD2 Identified by Pharmacophore-Based Virtual Screening." Biomolecules 12, no. 8 (July 29, 2022): 1054. http://dx.doi.org/10.3390/biom12081054.

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Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) is an innate immune pattern recognition receptor responsible for the recognition of bacterial peptidoglycan fragments. Given its central role in the formation of innate and adaptive immune responses, NOD2 represents a valuable target for modulation with agonists and antagonists. A major challenge in the discovery of novel small-molecule NOD2 modulators is the lack of a co-crystallized complex with a ligand, which has limited previous progress to ligand-based design approaches and high-throughput screening campaigns. To that end, a hybrid docking and pharmacophore modeling approach was used to identify key interactions between NOD2 ligands and residues in the putative ligand-binding site. Following docking of previously reported NOD2 ligands to a homology model of human NOD2, a structure-based pharmacophore model was created and used to virtually screen a library of commercially available compounds. Two compounds, 1 and 3, identified as hits by the pharmacophore model, exhibited NOD2 antagonist activity and are the first small-molecule NOD2 modulators identified by virtual screening to date. The newly identified NOD2 antagonist scaffolds represent valuable starting points for further optimization.

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24

Rica, Elena, Susana Álvarez, and Francesc Serratosa. "Ligand-Based Virtual Screening Based on the Graph Edit Distance." International Journal of Molecular Sciences 22, no. 23 (November 25, 2021): 12751. http://dx.doi.org/10.3390/ijms222312751.

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Chemical compounds can be represented as attributed graphs. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges. Nodes are individual components, and edges are relations between these components. In this case, pharmacophore-type node descriptions are represented by nodes and chemical bounds by edges. If we want to obtain the bioactivity dissimilarity between two chemical compounds, a distance between attributed graphs can be used. The Graph Edit Distance allows computing this distance, and it is defined as the cost of transforming one graph into another. Nevertheless, to define this dissimilarity, the transformation cost must be properly tuned. The aim of this paper is to analyse the structural-based screening methods to verify the quality of the Harper transformation costs proposal and to present an algorithm to learn these transformation costs such that the bioactivity dissimilarity is properly defined in a ligand-based virtual screening application. The goodness of the dissimilarity is represented by the classification accuracy. Six publicly available datasets—CAPST, DUD-E, GLL&GDD, NRLiSt-BDB, MUV and ULS-UDS—have been used to validate our methodology and show that with our learned costs, we obtain the highest ratios in identifying the bioactivity similarity in a structurally diverse group of molecules.

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25

Stiefl, Nikolaus, and Andrea Zaliani. "A Knowledge-Based Weighting Approach to Ligand-Based Virtual Screening." Journal of Chemical Information and Modeling 46, no. 2 (March 2006): 587–96. http://dx.doi.org/10.1021/ci050324c.

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26

Zhou, Hongyi, Hongnan Cao, and Jeffrey Skolnick. "FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening." Journal of Chemical Information and Modeling 61, no. 4 (March 16, 2021): 2074–89. http://dx.doi.org/10.1021/acs.jcim.0c01160.

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27

Himmat, Mubarak, Naomie Salim, Mohammed Al-Dabbagh, Faisal Saeed, and Ali Ahmed. "Adapting Document Similarity Measures for Ligand-Based Virtual Screening." Molecules 21, no. 4 (April 13, 2016): 476. http://dx.doi.org/10.3390/molecules21040476.

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28

Rathke, Fabian, Katja Hansen, Ulf Brefeld, and Klaus-Robert Müller. "StructRank: A New Approach for Ligand-Based Virtual Screening." Journal of Chemical Information and Modeling 51, no. 1 (December 17, 2010): 83–92. http://dx.doi.org/10.1021/ci100308f.

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29

Meissner, Kamila Anna, Thales Kronenberger, Vinícius Gonçalves Maltarollo, Gustavo Henrique Goulart Trossini, and Carsten Wrenger. "Targeting thePlasmodium falciparumplasmepsin V by ligand-based virtual screening." Chemical Biology & Drug Design 93, no. 3 (November 1, 2018): 300–312. http://dx.doi.org/10.1111/cbdd.13416.

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30

Al-Dabbagh, Mohammed Mumtaz, Naomie Salim, Mubarak Himmat, Ali Ahmed, and Faisal Saeed. "Quantum probability ranking principle for ligand-based virtual screening." Journal of Computer-Aided Molecular Design 31, no. 4 (February 20, 2017): 365–78. http://dx.doi.org/10.1007/s10822-016-0003-4.

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31

Ripphausen, Peter, Britta Nisius, and Jürgen Bajorath. "State-of-the-art in ligand-based virtual screening." Drug Discovery Today 16, no. 9-10 (May 2011): 372–76. http://dx.doi.org/10.1016/j.drudis.2011.02.011.

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32

Poli, Giulio, Carlotta Granchi, Flavio Rizzolio, and Tiziano Tuccinardi. "Application of MM-PBSA Methods in Virtual Screening." Molecules 25, no. 8 (April 23, 2020): 1971. http://dx.doi.org/10.3390/molecules25081971.

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Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.

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33

Vázquez, Javier, Manel López, Enric Gibert, Enric Herrero, and F. Javier Luque. "Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches." Molecules 25, no. 20 (October 15, 2020): 4723. http://dx.doi.org/10.3390/molecules25204723.

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Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

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34

Ahmed, Ali, Ammar Abdo, and Naomie Salim. "Ligand-Based Virtual Screening Using Bayesian Inference Network and Reweighted Fragments." Scientific World Journal 2012 (2012): 1–7. http://dx.doi.org/10.1100/2012/410914.

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Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.

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35

Seus, Vinicius Rosa, Giovanni Xavier Perazzo, Ana T. Winck, Adriano V. Werhli, and Karina S. Machado. "An Infrastructure to Mine Molecular Descriptors for Ligand Selection on Virtual Screening." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/325959.

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The receptor-ligand interaction evaluation is one important step in rational drug design. The databases that provide the structures of the ligands are growing on a daily basis. This makes it impossible to test all the ligands for a target receptor. Hence, a ligand selection before testing the ligands is needed. One possible approach is to evaluate a set of molecular descriptors. With the aim of describing the characteristics of promising compounds for a specific receptor we introduce a data warehouse-based infrastructure to mine molecular descriptors for virtual screening (VS). We performed experiments that consider as target the receptor HIV-1 protease and different compounds for this protein. A set of 9 molecular descriptors are taken as the predictive attributes and the free energy of binding is taken as a target attribute. By applying the J48 algorithm over the data we obtain decision tree models that achieved up to 84% of accuracy. The models indicate which molecular descriptors and their respective values are relevant to influence good FEB results. Using their rules we performed ligand selection on ZINC database. Our results show important reduction in ligands selection to be applied in VS experiments; for instance, the best selection model picked only 0.21% of the total amount of drug-like ligands.

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36

Issa, Naiem T., Stephen W. Byers, and Sivanesan Dakshanamurthy. "ES-Screen: A Novel Electrostatics-Driven Method for Drug Discovery Virtual Screening." International Journal of Molecular Sciences 23, no. 23 (November 27, 2022): 14830. http://dx.doi.org/10.3390/ijms232314830.

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Electrostatic interactions drive biomolecular interactions and associations. Computational modeling of electrostatics in biomolecular systems, such as protein-ligand, protein–protein, and protein-DNA, has provided atomistic insights into the binding process. In drug discovery, finding biologically plausible ligand-protein target interactions is challenging as current virtual screening and adjuvant techniques such as docking methods do not provide optimal treatment of electrostatic interactions. This study describes a novel electrostatics-driven virtual screening method called ‘ES-Screen’ that performs well across diverse protein target systems. ES-Screen provides a unique treatment of electrostatic interaction energies independent of total electrostatic free energy, typically employed by current software. Importantly, ES-Screen uses initial ligand pose input obtained from a receptor-based pharmacophore, thus independent of molecular docking. ES-Screen integrates individual polar and nonpolar replacement energies, which are the energy costs of replacing the cognate ligand for a target with a query ligand from the screening. This uniquely optimizes thermodynamic stability in electrostatic and nonpolar interactions relative to an experimentally determined stable binding state. ES-Screen also integrates chemometrics through shape and other physicochemical properties to prioritize query ligands with the greatest physicochemical similarities to the cognate ligand. The applicability of ES-Screen is demonstrated with in vitro experiments by identifying novel targets for many drugs. The present version includes a combination of many other descriptor components that, in a future version, will be purely based on electrostatics. Therefore, ES-Screen is a first-in-class unique electrostatics-driven virtual screening method with a unique implementation of replacement electrostatic interaction energies with broad applicability in drug discovery.

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37

Plewczynski, Dariusz, Stephane Spieser, and Uwe Koch. "Performance of Machine Learning Methods for Ligand-Based Virtual Screening." Combinatorial Chemistry & High Throughput Screening 12, no. 4 (May 1, 2009): 358–68. http://dx.doi.org/10.2174/138620709788167962.

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38

Bruno O. Villoutreix, Nicolas Renault, David Lagorce, Matthieu Montes, and Maria A. Miteva. "Free Resources to Assist Structure-Based Virtual Ligand Screening Experiments." Current Protein & Peptide Science 8, no. 4 (August 1, 2007): 381–411. http://dx.doi.org/10.2174/138920307781369391.

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39

Cavasotto, Claudio, and Andrew W. Orry. "Ligand Docking and Structure-based Virtual Screening in Drug Discovery." Current Topics in Medicinal Chemistry 7, no. 10 (May 1, 2007): 1006–14. http://dx.doi.org/10.2174/156802607780906753.

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40

Hamza, Adel, Ning-Ning Wei, and Chang-Guo Zhan. "Ligand-Based Virtual Screening Approach Using a New Scoring Function." Journal of Chemical Information and Modeling 52, no. 4 (April 9, 2012): 963–74. http://dx.doi.org/10.1021/ci200617d.

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41

Crisman, Thomas J., Mihiret T. Sisay, and Jürgen Bajorath. "Ligand-Target Interaction-Based Weighting of Substructures for Virtual Screening." Journal of Chemical Information and Modeling 48, no. 10 (September 27, 2008): 1955–64. http://dx.doi.org/10.1021/ci800229q.

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42

Arcon, Juan Pablo, Lucas A. Defelipe, Elias D. Lopez, Osvaldo Burastero, Carlos P. Modenutti, Xavier Barril, Marcelo A. Marti, and Adrian G. Turjanski. "Cosolvent-Based Protein Pharmacophore for Ligand Enrichment in Virtual Screening." Journal of Chemical Information and Modeling 59, no. 8 (August 2, 2019): 3572–83. http://dx.doi.org/10.1021/acs.jcim.9b00371.

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43

Baber, J. Christian, William A. Shirley, Yinghong Gao, and Miklos Feher. "The Use of Consensus Scoring in Ligand-Based Virtual Screening." Journal of Chemical Information and Modeling 46, no. 1 (January 2006): 277–88. http://dx.doi.org/10.1021/ci050296y.

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44

Jacobsson, Micael, and Anders Karlén. "Ligand Bias of Scoring Functions in Structure-Based Virtual Screening." Journal of Chemical Information and Modeling 46, no. 3 (May 2006): 1334–43. http://dx.doi.org/10.1021/ci050407t.

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45

Mochizuki, Masahiro, Shogo D. Suzuki, Keisuke Yanagisawa, Masahito Ohue, and Yutaka Akiyama. "QEX: target-specific druglikeness filter enhances ligand-based virtual screening." Molecular Diversity 23, no. 1 (July 3, 2018): 11–18. http://dx.doi.org/10.1007/s11030-018-9842-3.

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46

Prasad, Nirmal K., Vishnupriya Kanakaveti, Siddhartha Eadlapalli, Ramakrishna Vadde, Angamba Potshangbam Meetei, and Vaibhav Vindal. "Ligand-Based Pharmacophore Modeling and Virtual Screening of RAD9 Inhibitors." Journal of Chemistry 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/679459.

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Human RAD9 is a key cell-cycle checkpoint protein that participates in DNA repair, activation of multiple cell cycle phase checkpoints, and apoptosis. Aberrant RAD9 expression has been linked to breast, lung, thyroid, skin, and prostate tumorigenesis. Overexpression of RAD9 interacts with BCL-2 proteins and blocks the binding sites of BCL-2 family proteins to interact with chemotherapeutic drugs and leads to drug resistance. Focusing on this interaction, the present study was designed to identify the interaction sites of RAD9 to bind BCL-2 protein and also to inhibit RAD9-BCL-2 interactions by designing novel small molecule inhibitors using pharmacophore modeling and to restore BCL-2 for interacting with anticancer drugs. The bioactive molecules of natural origin act as excellent leads for new drug development. Thus, in the present study, we used the compounds of natural origin like camptothecin, ascididemin, and Dolastatin and also compared them with synthetic molecule NSC15520. The results revealed that camptothecin can act as an effective inhibitor among all the ligands taken and can be used as an RAD9 inhibitor. The amino acids ARG45 and ALA134 of RAD9 protein are interacting commonly with the drugs and BCL-2 protein.

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47

Chen, Beining, Robert F. Harrison, George Papadatos, Peter Willett, David J. Wood, Xiao Qing Lewell, Paulette Greenidge, and Nikolaus Stiefl. "Evaluation of machine-learning methods for ligand-based virtual screening." Journal of Computer-Aided Molecular Design 21, no. 1-3 (January 5, 2007): 53–62. http://dx.doi.org/10.1007/s10822-006-9096-5.

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48

von Behren, Mathias M., Stefan Bietz, Eva Nittinger, and Matthias Rarey. "mRAISE: an alternative algorithmic approach to ligand-based virtual screening." Journal of Computer-Aided Molecular Design 30, no. 8 (August 2016): 583–94. http://dx.doi.org/10.1007/s10822-016-9940-1.

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Drwal, Malgorzata N., and Renate Griffith. "Combination of ligand- and structure-based methods in virtual screening." Drug Discovery Today: Technologies 10, no. 3 (September 2013): e395-e401. http://dx.doi.org/10.1016/j.ddtec.2013.02.002.

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Li, Xiao Yan, Xue Liu, Chao Feng Du, Hua Jun Luo, Wei Qiao Deng, and Nian Yu Huang. "Structure-Based Virtual Screening of Compound Library for Anti-Estrogen Breast Cancer Candidates." Advanced Materials Research 884-885 (January 2014): 531–34. http://dx.doi.org/10.4028/www.scientific.net/amr.884-885.531.

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Abstract:

We reported a structure-based virtual screening of acompound library derived from 3-acyl-5-hydroxybenzofurans to develop potentialdrugs for treating breast cancer. A library of 160,000 compounds was generatedand screened based on the G-score between ligands and receptor ERα. The topstructures were further analyzed to evaluate the receptor-ligand bindinginteractions. By comparing the binding characteristics and docking scoringvalues to the existing ERα analogues, we determined top 200 compounds aspotential drug candidates for breast cancer.

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