Canvas

A comprehensive cheminformatics computing environment

The Advantages of Cheminformatics

The discovery of novel therapeutic agents and materials requires exploring increasingly expanded and complex chemical space, resulting in rapidly growing data proliferation. Computational techniques that scale favorably with the expanding chemical space and provide efficient insights have become critical to any lead discovery and lead optimization effort.

Cheminformatics techniques such as fingerprint-based similarity searching and substructure matching can screen millions of compounds in seconds; clustering and diversity selection can analyze and improve the content of real and virtual compound libraries; principal components analysis and self-organizing maps reduce complex, high dimensional information into easily visualized relationships in a small number of dimensions; and supervised learning techniques offer quantitative models that elucidate structure-activity relationships and provide insights into new compounds' activities.

Cross-platform, cutting edge user interface:
Built from the ground up, the Canvas graphical interface was designed with scalability in mind, and features a chemical spreadsheet with smooth, instantaneous access to millions of structures and thousand of properties, all of which are stored in an infinitely expandable relational database.

Custom views:
Users can save a snapshot of any particular set of rows and columns, which can be recalled and modified at any later time and combined with other custom views using various logical operations.

Unparalleled fingerprinting capabilities:
Canvas offers seven types of hashed fingerprints, MACCS keys, and customizable SMARTS-base structural keys. All popular fingerprinting methods are represented, and a sparse storage scheme allows each chemical feature to be mapped to a unique bit.

Ultra-fast substructure searching:
As structures are imported into a Canvas project, a 2D index is automatically built in the background, which allows the user to search millions of compounds in mere seconds.

R-group analysis:
Explore activity of a congeneric series based on R-group composition.

Scaffold Decomposition:
Classify and explore a set of structures based on the scaffolds they contain.

Chemistry filters:
Users can create custom rules to prohibit or require the presence of any number of different chemical features, or use the built-in REOS filter.

Data analysis and visualization:
Univariate and bivariate statistics, scatter plots, histograms, pie charts, and heat maps allow the user to spot trends and rapidly drill down to critical pieces of information.

Maximum common substructure:
A unique, innovative methodology rigorously identifies maximum common substructures matching any number of compounds at a speed that is orders of magnitude faster than conventional algorithms.

Scientific applications:
Extensive access to tools for property calculations, clustering, diversity, classification, QSAR, and data reduction.

Python toolkit:
The core Canvas engine is exposed through a Python API (application programming interface), which provides a comprehensive, object-oriented library that allows users to develop custom applications.

Citations and Acknowledgements

Schrödinger Release 2021-4: Canvas, Schrödinger, LLC, New York, NY, 2021.

ö Duan, J.; Dixon, S.L.; Lowrie, J.F.; Sherman, W., "Analysis and Comparison of 2D Fingerprints: Insights into Database Screening Performance Using Eight Fingerprint Methods," J. Molec. Graph. Model., 2010, 29, 157-170

ö Sastry, M.; Lowrie, J.F.; Dixon, S.L.; Sherman, W., "Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments," J. Chem. Inf. Model., 2010, 50, 771-784

"Investigating DNA Adduct Formation by Flavor Chemicals and Tobacco Byproducts in Electronic Nicotine Delivery System (ENDS) using in silico Approaches"

Kang, J.(C.); Valerio Jr., L.G., Toxicology and Applied Pharmacology, 2020, 398, 115026

ö "High throughput evaluation of macrocyclization strategies for conformer stabilization"

Sindhikara, D. and Borrelli, K., Nature, Scientific Reports , 2018, 8 (6585), doi:10.1038/s41598-018-24766-5

"Bitter or not? BitterPredict, a tool for predicting taste from chemical structure"

Dagan-Wiener, A.; Nissim, I.; Ben Abu, N.; Borgonovo, G.; Bassoli, A.; Niv, M.Y., Scientific Reports, 2017, 7(12074), 12074

ö "AutoQSAR: An Automated Machine Learning Tool for Best-Practice QSAR Modeling"

Dixon, S.L.; Duan, J.; Smith, E.; Von Bargen, C.D.; Sherman, W.; Repasky, M.P., Future Med. Chem., 2016, 8 (15), 1825-1839

"Discovery of Thienoquinolone Derivatives as Selective and ATP Non-Competitive CDK5/p25 Inhibitors by Structure-Based Virtual Screening"

Chatterjee, A.; Cutler, S.J.; Doerksen, R.J.; Khan, I.A.; Williamson, J.S., Bioorg. Med. Chem., 2014, 22, 6409-6421

ö "Boosting virtual screening enrichments with data fusion: Coalescing hits from two-dimensional fingerprints, shape, and docking"

Sastry, G.M.; Inakollu, V.S.; Sherman, W, J. Chem. Inf. Model., 2013, 53, 1531-1542

ö "Lead optimization mapper: Automating free energy calculations for lead optimization"

Liu, S.; Wu, Y.; Lin, T.; Abel, R.; Redmann, J.P.; Summa, C.M.; Jaber, V.R.; Lim, N.M.; Mobley, D.L., J. Comput. Aided Mol. Des., 2013, 27(9), 755-770

ö "Kernel-based partial least squares: Application to fingerprint-based QSAR with model visualization"

An, Y.; Sherman, W.; Dixon, S.L., J. Chem. Inf. Model., 2013, 53(9), 2312-2321

"Fragment-based hit identification: Thinking in 3D"

Morley, A.D.; Pugliese, A.; Birchall, K.; Bower, J.; Brennan, P.; Brown, N.; Chapman, T.; Drysdale, M.; Gilbert, I.H.; Hoelder, S.; Jordan, A.; Ley, S.V.; Merritt, A.; Miller, D.; Swarbrick, M.E.; Wyatt, P.G., Drug Discov Today, 2013, 18(23-24), 1221-1227

ö "Hole filling and library optimization: Application to commercially available fragment libraries"

An, Y.; Sherman, W.; Dixon, S.L., Bioorg. Med. Chem., 2012, 20, 5379–5387

"Identification of novel human dipeptidyl peptidase-IV inhibitors of natural origin (part I): Virtual screening and activity assays"

Guasch, L.; Ojeda, M. J.; González-Abuin, N.; Sala, E.; Cereto-Massagué, A.; Mulero, M.; Valls, C.; Pinent, M.; Ardévol, A.; Garcia-Vallvé, S.; Pujadas, G., PLoS One, 2012, 7(9), e44971

ö "Generation of receptor structural ensembles for virtual screening using binding site shape analysis and clustering"

Osguthorpe, D.J.; Sherman, W.; Hagler, A.T., Chem. Biol. Drug Des., 2012, 80(2), 182-193

ö "Exploring protein flexibility: Incorporating structural ensembles from crystal structures and simulation into virtual screening protocols"

Osguthorpe D.J.; Sherman, W.; Hagler, A.T., J. Phys. Chem. B, 2012, 116(23), 6952-6959

ö "Consensus Induced Fit Docking (cIFD): Methodology, validation, and application to the discovery of novel Crm1 inhibitors"

Kalid, O.; Warshaviak, D.T.; Shechter, S.; Sherman, W.; Shacham, S., J. Comput. Aided Mol. Des., 2012, 26, 1217–1228

ö "Diversity-oriented synthesis of a library of substituted tetrahydropyrones using oxidative carbon-hydrogen bond activation and click chemistry"

Zaware, N.; LaPorte, M.G.; Farid, R.; Liu, L.; Wipf, P.; Floreancig, P.E., Molecules, 2011, 16, 3648-3662

ö "Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments"

Sastry, M.; Lowrie, J.F.; Dixon, S.L.; Sherman, W., J. Chem. Inf. Model., 2010, 50, 771-784

ö "Analysis and Comparison of 2D Fingerprints: Insights into Database Screening Performance Using Eight Fingerprint Methods"

Duan, J.; Dixon, S.L.; Lowrie, J.F.; Sherman, W., J. Molec. Graph. Model., 2010, 29, 157-170
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