Computational Drug Discovery Methods

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.

A new and rapid colorimetric determination of acetylcholinesterase activity

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique.

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

ZINC: A Free Tool to Discover Chemistry for Biology

ZINC is a free public resource for ligand discovery. The database contains over twenty million commercially available molecules in biologically relevant representations that may be downloaded in popular ready-to-dock formats and subsets. The Web site also enables searches by structure, biological activity, physical property, vendor, catalog number, name, and CAS number. Small custom subsets may be created, edited, shared, docked, downloaded, and conveyed to a vendor for purchase. The database is maintained and curated for a high purchasing success rate and is freely available at zinc.docking.org.

Chemistry and Biology Of Multicomponent Reactions

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTChemistry and Biology Of Multicomponent ReactionsAlexander Dömling*†‡, Wei Wang†§, and Kan Wang†View Author Information† Department of Pharmaceutical Sciences, University of Pittsburgh, Biomedical Science Tower 3, Suite 10019, 3501 Fifth Avenue, Pittsburgh, Pennsylvania 15261, United States‡ Chair of Drug Design, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands§ Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, and School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, P. R. China*E-mail: [email protected]. Fax: (+)412-383-5298.Cite this: Chem. Rev. 2012, 112, 6, 3083–3135Publication Date (Web):March 22, 2012Publication History Received23 July 2010Published online22 March 2012Published inissue 13 June 2012https://pubs.acs.org/doi/10.1021/cr100233rhttps://doi.org/10.1021/cr100233rreview-articleACS PublicationsCopyright © 2012 American Chemical SocietyRequest reuse permissionsArticle Views31588Altmetric-Citations1987LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Inhibitors,Peptides and proteins,Reaction products,Receptors,Scaffolds Get e-Alerts

Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis

Suppression effect in multiple regression analysis may be more common in research than what is currently recognized. We have reviewed several literatures of interest which treats the concept and types of suppressor variables. Also, we have highlighted systematic ways to identify suppression effect in multiple regressions using statistics such as: R2, sum of squares, regression weight and comparing zero-order correlations with Variance Inflation Factor (VIF) respectively. We also establish that suppression effect is a function of multicollinearity; however, a suppressor variable should only be allowed in a regression analysis if its VIF is less than five (5).

Cancer Biology and Therapeutics: Navigating Recent Advances and Charting Future Directions

Cancer, a multifaceted and heterogeneous ailment, persists as a substantial global public health concern. Recent strides in cancer biology have profoundly augmented our comprehension of the intricate genetic and molecular mechanisms underlying the inception and advancement of cancer. This headway has propelled the formulation of innovative therapeutic strategies, embracing targeted interventions and immunotherapies, which have showcased promising outcomes in clinical assessments. However, a multitude of obstacles continue to challenge the creation of efficacious and individualized cancer treatments. This systematic review encapsulates an extensive vista of contemporary advancements in cancer biology and therapeutics, accentuating pivotal breakthroughs in our grasp of cancer's intricacies, while illuminating the latest therapeutic avenues for cancer management. Moreover, the paper elaborates on the quest's upcoming trajectories in cancer research and treatment, encompassing burgeoning technologies and uncharted domains of inquiry that might pave the way for more efficacious and personalized interventions for individuals afflicted by cancer.