Academic literature on the topic 'Virtual screening of compounds and experimental validation'
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Journal articles on the topic "Virtual screening of compounds and experimental validation"
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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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Dotolo, Serena, Carmen Cervellera, Maria Russo, Gian Luigi Russo, and Angelo Facchiano. "Virtual Screening of Natural Compounds as Potential PI3K-AKT1 Signaling Pathway Inhibitors and Experimental Validation." Molecules 26, no. 2 (January 18, 2021): 492. http://dx.doi.org/10.3390/molecules26020492.
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A computational screening for natural compounds suitable to bind the AKT protein has been performed after the generation of a pharmacophore model based on the experimental structure of AKT1 complexed with IQO, a well-known inhibitor. The compounds resulted as being most suitable from the screening have been further investigated by molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis and toxicity profiles. Two compounds selected at the end of the computational analysis, i.e., ZINC2429155 (also named STL1) and ZINC1447881 (also named AC1), have been tested in an experimental assay, together with IQO as a positive control and quercetin as a negative control. Only STL1 clearly inhibited AKT activation negatively modulating the PI3K/AKT pathway.
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Lewis, Stephanie N., Josep Bassaganya-Riera, and David R. Bevan. "Virtual Screening as a Technique for PPAR Modulator Discovery." PPAR Research 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/861238.
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Virtual screening (VS) is a discovery technique to identify novel compounds with therapeutic and preventive efficacy against disease. Our current focus is on the in silico screening and discovery of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists. It is well recognized that PPARγagonists have therapeutic applications as insulin sensitizers in type 2 diabetes or as anti-inflammatories. VS is a cost- and time-effective means for identifying small molecules that have therapeutic potential. Our long-term goal is to devise computational approaches for testing the PPARγ-binding activity of extensive naturally occurring compound libraries prior to testing agonist activity using ligand-binding and reporter assays. This review summarizes the high potential for obtaining further fundamental understanding of PPARγbiology and development of novel therapies for treating chronic inflammatory diseases through evolution and implementation of computational screening processes for immunotherapeutics in conjunction with experimental methods for calibration and validation of results.
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Ferreira, Letícia Tiburcio, Joyce V. B. Borba, José Teófilo Moreira-Filho, Aline Rimoldi, Carolina Horta Andrade, and Fabio Trindade Maranhão Costa. "QSAR-Based Virtual Screening of Natural Products Database for Identification of Potent Antimalarial Hits." Biomolecules 11, no. 3 (March 19, 2021): 459. http://dx.doi.org/10.3390/biom11030459.
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With about 400,000 annual deaths worldwide, malaria remains a public health burden in tropical and subtropical areas, especially in low-income countries. Selection of drug-resistant Plasmodium strains has driven the need to explore novel antimalarial compounds with diverse modes of action. In this context, biodiversity has been widely exploited as a resourceful channel of biologically active compounds, as exemplified by antimalarial drugs such as quinine and artemisinin, derived from natural products. Thus, combining a natural product library and quantitative structure–activity relationship (QSAR)-based virtual screening, we have prioritized genuine and derivative natural compounds with potential antimalarial activity prior to in vitro testing. Experimental validation against cultured chloroquine-sensitive and multi-drug-resistant P. falciparum strains confirmed the potent and selective activity of two sesquiterpene lactones (LDT-597 and LDT-598) identified in silico. Quantitative structure–property relationship (QSPR) models predicted absorption, distribution, metabolism, and excretion (ADME) and physiologically based pharmacokinetic (PBPK) parameters for the most promising compound, showing that it presents good physiologically based pharmacokinetic properties both in rats and humans. Altogether, the in vitro parasite growth inhibition results obtained from in silico screened compounds encourage the use of virtual screening campaigns for identification of promising natural compound-based antimalarial molecules.
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Scarpino, Andrea, László Petri, Damijan Knez, Tímea Imre, Péter Ábrányi-Balogh, György G. Ferenczy, Stanislav Gobec, and György M. Keserű. "WIDOCK: a reactive docking protocol for virtual screening of covalent inhibitors." Journal of Computer-Aided Molecular Design 35, no. 2 (January 18, 2021): 223–44. http://dx.doi.org/10.1007/s10822-020-00371-5.
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AbstractHere we present WIDOCK, a virtual screening protocol that supports the selection of diverse electrophiles as covalent inhibitors by incorporating ligand reactivity towards cysteine residues into AutoDock4. WIDOCK applies the reactive docking method (Backus et al. in Nature 534:570–574, 2016) and extends it into a virtual screening tool by introducing facile experimental or computational parametrization and a ligand focused evaluation scheme together with a retrospective and prospective validation against various therapeutically relevant targets. Parameters accounting for ligand reactivity are derived from experimental reaction kinetic data or alternatively from computed reaction barriers. The performance of this docking protocol was first evaluated by investigating compound series with diverse warhead chemotypes against KRASG12C, MurA and cathepsin B. In addition, WIDOCK was challenged on larger electrophilic libraries screened against OTUB2 and NUDT7. These retrospective analyses showed high sensitivity in retrieving experimental actives, by also leading to superior ROC curves, AUC values and better enrichments than the standard covalent docking tool available in AutoDock4 when compound collections with diverse warheads were investigated. Finally, we applied WIDOCK for the prospective identification of covalent human MAO-A inhibitors acting via a new mechanism by binding to Cys323. The inhibitory activity of several predicted compounds was experimentally confirmed and the labelling of Cys323 was proved by subsequent MS/MS measurements. These findings demonstrate the usefulness of WIDOCK as a warhead-sensitive, covalent virtual screening protocol.
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Rahman, A. S. M. Zisanur, Chengyou Liu, Hunter Sturm, Andrew M. Hogan, Rebecca Davis, Pingzhao Hu, and Silvia T. Cardona. "A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery." PLOS Computational Biology 18, no. 10 (October 13, 2022): e1010613. http://dx.doi.org/10.1371/journal.pcbi.1010613.
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Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their growth inhibitory activity (hit rate 0.87%) against the antibiotic-resistant bacterium Burkholderia cenocepacia and described their molecular features with a directed-message passing neural network (D-MPNN). Then, we used the data to train an ML model that achieved a receiver operating characteristic (ROC) score of 0.823 on the test set. Finally, we predicted antibacterial activity in virtual libraries corresponding to 1,614 compounds from the Food and Drug Administration (FDA)-approved list and 224,205 natural products. Hit rates of 26% and 12%, respectively, were obtained when we tested the top-ranked predicted compounds for growth inhibitory activity against B. cenocepacia, which represents at least a 14-fold increase from the previous hit rate. In addition, more than 51% of the predicted antibacterial natural compounds inhibited ESKAPE pathogens showing that predictions expand beyond the organism-specific dataset to a broad range of bacteria. Overall, the developed ML approach can be used for compound prioritization before screening, increasing the typical hit rate of drug discovery.
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Ji, Xin, Zhensheng Wang, Qianqian Chen, Jingzhong Li, Heng Wang, Zenglei Wang, and Lan Yang. "In Silico and In Vitro Antimalarial Screening and Validation Targeting Plasmodium falciparum Plasmepsin V." Molecules 27, no. 9 (April 21, 2022): 2670. http://dx.doi.org/10.3390/molecules27092670.
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Malaria chemotherapy is greatly threatened by the recent emergence and spread of resistance in the Plasmodium falciparum parasite against artemisinins and their partner drugs. Therefore, it is an urgent priority to develop new antimalarials. Plasmepsin V (PMV) is regarded as a superior drug target for its essential role in protein export. In this study, we performed virtual screening based on homology modeling of PMV structure, molecular docking and pharmacophore model analysis against a library with 1,535,478 compounds, which yielded 233 hits. Their antimalarial activities were assessed amongst four non-peptidomimetic compounds that demonstrated the promising inhibition of parasite growth, with mean IC50 values of 6.67 μM, 5.10 μM, 12.55 μM and 8.31 μM. No significant affection to the viability of L929 cells was detected in these candidates. These four compounds displayed strong binding activities with the PfPMV model through H-bond, hydrophobic, halogen bond or π-π interactions in molecular docking, with binding scores under −9.0 kcal/mol. The experimental validation of molecule-protein interaction identified the binding of four compounds with multiple plasmepsins; however, only compound 47 showed interaction with plasmepsin V, which exhibited the potential to be developed as an active PfPMV inhibitor.
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Zhu, Hui, Yulin Zhang, Wei Li, and Niu Huang. "A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years." International Journal of Molecular Sciences 23, no. 24 (December 15, 2022): 15961. http://dx.doi.org/10.3390/ijms232415961.
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Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.
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Wang, Jing, Yu Jiang, Yingnan Wu, Hui Yu, Zhanli Wang, and Yuheng Ma. "Pharmacophore-Based Virtual Screening of Potential SARS-CoV-2 Main Protease Inhibitors from Library of Natural Products." Natural Product Communications 17, no. 12 (December 2022): 1934578X2211436. http://dx.doi.org/10.1177/1934578x221143635.
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Background: The SARS-CoV-2 main protease (Mpro) is an attractive target for drug discovery. Methods: A pharmacophore model was built using the three-dimensional (3D) pharmacophore generation algorithm HypoGen in Discovery Studio 2019. The best pharmacophore model was selected for validation using a test set of 24 compounds and was used as a 3D query for further screening of an in-house database of natural compounds. Lipinski's rule of five was used to assess the drug-like properties of the hit compounds. The filtered compounds were then subjected to bioactivity evaluations. The active compounds were docked into the active site of the SARS-CoV-2 Mpro crystal structure (PDB ID: 7D1M). Results: A suitable 3D pharmacophore model, Hypo1, was found to be the best model, consisting of four features (one hydrophobic feature, one hydrogen bond donor, and two hydrogen bond acceptors). Pharmacophore-based virtual screening with Hypo1 as the query to search an in-house database of 34 439 natural compounds resulted in 1502 hits. Among these, 255 compounds satisfied Lipinski's rule of five. The highest ranking 10 compounds were selected for further experimental testing, and one hit (W-7) illustrated inhibitory activity against SARS-CoV-2 Mpro with an IC50 value of 75 μM. Docking studies revealed that this hit compound retained the necessary interactions within the active site of SARS-CoV-2 Mpro. Conclusion The identified lead natural compound could provide a scaffold for the further development of SARS-CoV-2 Mpro inhibitors.
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Bajusz, Dávid, Zsolt Bognár, Jessica Ebner, Florian Grebien, and György M. Keserű. "Discovery of a Non-Nucleoside SETD2 Methyltransferase Inhibitor against Acute Myeloid Leukemia." International Journal of Molecular Sciences 22, no. 18 (September 17, 2021): 10055. http://dx.doi.org/10.3390/ijms221810055.
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Histone methyltransferases (HMTs) have attracted considerable attention as potential targets for pharmaceutical intervention in various malignant diseases. These enzymes are known for introducing methyl marks at specific locations of histone proteins, creating a complex system that regulates epigenetic control of gene expression and cell differentiation. Here, we describe the identification of first-generation cell-permeable non-nucleoside type inhibitors of SETD2, the only mammalian HMT that is able to tri-methylate the K36 residue of histone H3. By generating the epigenetic mark H3K36me3, SETD2 is involved in the progression of acute myeloid leukemia. We developed a structure-based virtual screening protocol that was first validated in retrospective studies. Next, prospective screening was performed on a large library of commercially available compounds. Experimental validation of 22 virtual hits led to the discovery of three compounds that showed dose-dependent inhibition of the enzymatic activity of SETD2. Compound C13 effectively blocked the proliferation of two acute myeloid leukemia (AML) cell lines with MLL rearrangements and led to decreased H3K36me3 levels, prioritizing this chemotype as a viable chemical starting point for drug discovery projects.
Dissertations / Theses on the topic "Virtual screening of compounds and experimental validation"
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Noeske, Tobias [Verfasser]. "Allosteric modulators of metabotropic glutamate receptors : from virtual screening to experimental validation / Tobias Noeske." 2007. http://d-nb.info/984886036/34.
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Chakraborti, Sohini. "Protein-small molecule interactions: Structural insights and applications in computational drug discovery." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5520.
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Deviation from normal healthy conditions, termed as disease, can often be triggered due to the malfunctioning of proteins. Modulating the functions of proteins by administering therapeutic agents (drugs) may alleviate the disease conditions. The majority of the drugs currently available in the market are small organic molecules due to their pharmacological and commercial advantages. These small molecule drugs interact with the protein targets through specific sites on the surface of the protein structure (binding sites). Thus, the structural data of protein-small molecule complexes forms a crucial starting point for most drug discovery programs. The work reported in this thesis deals with understanding various aspects of protein-small molecule interactions. The thesis begins (Chapter 1) with a general introduction on the implication of proteins structural data in drug discovery programs. Chapter 2 provides a fundamental understanding of the general trend in local quality of protein-small molecule crystal complexes deposited in the Protein Data Bank (PDB). Our results suggest ‘seeing is not always believing’ and aims to sensitize the non-crystallographer user community that high-resolution need not always guarantee confident small molecule binding poses. The study indicates 35% of the inspected ~0.28 million protein-small molecule binding site pairs available from ~66000 PDB entries, need serious attention before using those as input in any important applications. Results reported in Chapter 3 suggest that the stereochemical quality of bound small molecules generally agrees well with their crystallographic quality. The findings from this work could be the stepping-stones for developing structure determination technique-independent ligand pose validation tools. The learning from Chapter 3 is extended to Chapter 4 to investigate the stereochemical quality of the small molecules bound to protein structures determined by cryo-EM. Our data shows that the stereochemical quality of small molecules bound to high-resolution protein structures determined by cryo-EM is comparable to high-quality small molecules bound to protein crystal structures. Chapter 5 presents a computational analysis aimed at providing insights into the molecular basis of the specificity of a novel anti-tubercular compound, NU-6027 (identified in a phenotypic screening by experimental collaborators), towards two out of the eleven known Serine-Threonine Protein Kinases in Mycobacterium tuberculosis (Mtb). Chapter 6 reports the development of a freely available web server that facilitates the identification of new uses of old drugs and aid in drug repurposing. In Chapter 7, the principles of ‘neighborhood behavior’ are exploited to identify potential known drugs that could be repurposed against the main protease of SARS-CoV-2. Chapter 8 discusses a virtual screening strategy to identify potential binders of a novel Mtb target, Rv1636 (or the Universal Stress Protein). Collaborators have experimentally validated some of the compounds shortlisted from the computational studies. Chapter 9 summarizes the findings from work reported in the entire thesis and future applications. Overall, this thesis inspects protein-small molecule complexes from a local perspective, aiding the design of rigorous computational experiments that can contribute to solving global unmet medical needs.
Interested readers may contact the author directly for Supplementary data at "sohini@iisc.ac.in"DST-INSPIRE fellowship
Book chapters on the topic "Virtual screening of compounds and experimental validation"
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Barakat, Khaled H., Jonathan Y. Mane, and Jack A. Tuszynski. "Virtual Screening." In Handbook of Research on Computational and Systems Biology, 28–60. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-491-2.ch002.
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Virtual screening, or VS, is emerging as a valuable tool in discovering new candidate inhibitors for many biologically relevant targets including the many chemotherapeutic targets that play key roles in cell signaling pathways. However, despite the great advances made in the field thus far, VS is still in constant development with a relatively low success rate that needs to be improved by parallel experimental validation methods. This chapter reviews the recent advances in VS, focusing on the range and type of computational methods and their successful applications in drug discovery. The chapter also discusses both the advantages and limitations of the various techniques used in VS and outlines a number of future directions in which the field may progress.
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E.S. Mosa, Farag, Ayman O.S. El-Kadi, and Khaled Barakat. "Targeting the Aryl Hydrocarbon Receptor (AhR): A Review of the In-Silico Screening Approaches to Identify AhR Modulators." In High-Throughput Screening for Drug Discovery [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99228.
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Aryl hydrocarbon receptor (AhR) is a biological sensor that integrates environmental, metabolic, and endogenous signals to control complex cellular responses in physiological and pathophysiological functions. The full-length AhR encompasses various domains, including a bHLH, a PAS A, a PAS B, and transactivation domains. With the exception of the PAS B and transactivation domains, the available 3D structures of AhR revealed structural details of its subdomains interactions as well as its interaction with other protein partners. Towards screening for novel AhR modulators homology modeling was employed to develop AhR-PAS B domain models. These models were validated using molecular dynamics simulations and binding site identification methods. Furthermore, docking of well-known AhR ligands assisted in confirming these binding pockets and discovering critical residues to host these ligands. In this context, virtual screening utilizing both ligand-based and structure-based methods screened large databases of small molecules to identify novel AhR agonists or antagonists and suggest hits from these screens for validation in an experimental biological test. Recently, machine-learning algorithms are being explored as a tool to enhance the screening process of AhR modulators and to minimize the errors associated with structure-based methods. This chapter reviews all in silico screening that were focused on identifying AhR modulators and discusses future perspectives towards this goal.
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Modanwal, Shristi, Viswajit Mulpuru, and Nidhi Mishra. "Computational Drug Discovery Against COVID-19." In COVID-19: Origin, Impact and Management (Part 2), 96–110. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165944123010010.
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The global spread of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), which causes the disease COVID-19, has increased drastically since the first cases in Wuhan, People's Republic of China, at the end of 2019. There is no single drug that can be used specifically to treat COVID. The crucial stage in the drug development process is screening huge libraries of bioactive molecules against a biological target, usually a receptor or a protein. Virtual Screening (VS) has become a valuable tool in the drug development process as it allows for efficient in silico searches of millions of compounds, resulting in higher yields of possible therapeutic leads, and is cost-effective. The spread of the SARS-CoV-2 virus presents a major threat to world health and has resulted in a global crisis because of the high mortality rate and absence of clinically authorised treatments and vaccines for COVID-19. Finding effective drugs or repurposing available antiviral drugs is a critical need in the fight against COVID-19. VS can be classified as either Structural-Based Virtual Screening or Ligand-Based Virtual Screening. VS techniques have been widely applied in the field of antiviral drug design and have aided in the identification of new compounds as possible anti-viral drugs. Both LBVS and SBVS approaches have proved extremely helpful in identifying several prospective anti-viral drugs with nanomolar range. VS, in contrast to experimental approaches, is quick and cost-effective on the one side but has low prediction accuracy on the other.
Conference papers on the topic "Virtual screening of compounds and experimental validation"
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Marrero-Ponce, Yovani, Alina Montero-Torres, Maité Iyarreta-Veitía, Carlos Romero-Zaldivar, Carlos Brandt, Priscilla Ávila, Karin Kirchgatter, and Yanetsy Machado. "A Novel Approach for Computer-Aided “Rational” Drug Design: Theoretical and Experimental Assessment of a Promising Method for Virtual Screening and in silico Design of New Antimalarial Compounds." In The 9th International Electronic Conference on Synthetic Organic Chemistry. Basel, Switzerland: MDPI, 2005. http://dx.doi.org/10.3390/ecsoc-9-01664.
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Ramirez, Jason, Christine Lee, Elliot Wallace, and Kristen Lindgren. "Development and Initial Validation of Marijuana Identity Implicit Associations Tests among Late Adolescents in Washington State." In 2021 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.01.000.13.
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The current climate surrounding adolescent marijuana use in the U.S. is facing unprecedented circumstances. Rates of daily use are at or near all-time highs and perceptions of risk are at an all-time low in the history of the Monitoring the Future study among 8th, 10th, and 12th graders. These rates are occurring despite research demonstrating worse long-term health outcomes associated with earlier age of marijuana use onset and increasing THC levels among marijuana products. As a result, there is an urgent need to identify risk factors that may represent screening markers of risk or targets for prevention and intervention among adolescents. One important risk factor for alcohol and tobacco is the extent to which one identifies with each substance. This aspect of identity can be measured with adaptations of the Implicit Association Test (IAT), a reaction time measure that aims to assess associations held in memory between constructs (e.g., marijuana and one’s self-concept). The aim of the current study was to develop and test two Marijuana Identity IATs among late adolescents in Washington State, one using images and another using words to represent marijuana and its control category. The current study included 169 adolescents between the ages of 15-18 (Mean age = 16.9, SD age = 0.9, 50% female, 66% high school student) with recruitment stratified by marijuana use (to include participants that range from non-users to heavy users) and gender. Data described here come from the online baseline assessment that included the Marijuana Identity IATs and self-report measures of marijuana use, consequences, and explicit (i.e., self-reported) marijuana identification. Results from the IATs reveal two normal distributions of IAT scores that were both positive on average indicating faster reaction times when marijuana was categorized with the self (and a neutral category categorized with other people). Split-half reliabilities of the IATs revealed internal consistencies in the range of previous substance-related IATs (word-based IAT, r = 0.52; imaged-based IAT, r = 0.40). In negative binomial regression models that controlled for age and sex, both IATs were significantly associated with use and consequences such that faster reaction times categorizing marijuana with the self were associated with more marijuana use and consequences (ps< .01). When controlling for self-reported identification marijuana, only the image-based IAT was significantly associated with marijuana use and consequences (ps< .05). The findings demonstrate relationships between IAT performance and marijuana use outcomes that compare favorably to past marijuana-related IATs lending support to implicit associations between the marijuana and the self as an important marker of marijuana use behaviors. Despite this promise, the relative inferiority of the internal consistency of these IATs to self-report measures may limit their utility as tools for screening. Future experimental and longitudinal research is warranted however, to examine identification with marijuana as a causal candidate for marijuana misuse to examine its potential as a prevention and intervention target.