Please see https://smallmoleculesuite.org/ for the latest version. This repository has been archived for reference purposes only.
This repository contains three related web applications that were developed as part of the NIH LINCS Consortium in a collaboration between Harvard Medical School's Sorger Lab and Laboratory of Systems Pharmacology (LSP) and the University of Cincinnati's Laboratory for Statistical Genomics and Systems Biology. The former (HMS) is one of the LINCS Data and Signature Generation Centers (DSGCs) and the latter (Cincinnati) is part of the BD2K-LINCS Data Coordination and Integration Center (DCIC).
All of the web applications are implemented in R, using the Shiny framework for interactive applications.
The apps may be visited on the web at www.smallmoleculesuite.org or run offline in a local R installation (see details below) or via Docker. A docker image containing the application, as well as version-controlled instances of all of its dependencies, is available on DockerHub. Details on how to install and run Docker on Linux, macOS, and Windows can be found here.
Publication:
Nienke Moret, Nicholas A. Clark, Marc Hafner, Yuan Wang, Eugen Lounkine, Mario Medvedovic, Jinhua Wang, Nathanael Gray, Jeremy Jenkins, Peter K. Sorger. Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries. Cell Chem Biol (2019). doi:10.1016/j.chembiol.2019.02.018
### Install R packages using Microsoft MRAN repository to ensure correct version
install.packages('readr', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('shiny', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('shiny.semantic', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('shinyjs', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('DT', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('dplyr', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('crosstalk', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('plotly', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('markdown', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('clipr', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')
install.packages('rclipboard', repos = 'https://mran.microsoft.com/snapshot/2017-11-28')SelectivitySelectR shows the affinity and selectivity of compounds in the HMS-LINCS collection for a gene of interest. To use SelectivitySelectR, first select your target of interest and binding criteria. Subsequently, select a region in the main plot with compounds of your interest. You can then select three compounds in the bottom table and view their known binding affinities in detail.
Visit on the web:
http://www.smallmoleculesuite.org/apps/SelectivitySelectR/
Or run locally in R:
shiny::runGitHub("sorgerlab/smallmoleculesuite", subdir = "SelectivitySelectR")SimilaritySelectR shows the similarity of compounds in the HMS-LINCS collection to a reference compound. Similarity is regarded in threefold: structural similarity (Tanimoto similarity of Morgan2 fingerprints— calculated using RDKit), target affinity spectrum similarity (TAS) and phenotypic fingerprint similarity (PFP). To use SimilaritySelectR, select a reference compound and adjust filters as desired. From the main plots, select a region with compounds of interest. Then, select up to three compounds in the bottom table and view their known binding affinities in detail.
Visit on the web:
http://www.smallmoleculesuite.org/apps/SimilaritySelectR/
Or run locally in R:
shiny::runGitHub("sorgerlab/smallmoleculesuite", subdir = "SimilaritySelectR")LibraryR composes custom chemical genetics libraries for gene-sets of interest. Compounds in the library are selected based on affinity, selectivity, structural similarity (calculated using RDKit) and clinical development phase. Additionally we source several expert opinion "best-in-class" lists. To use LibraryR, simply submit a list of genes for which the library should be designed, or load one of the example gene-sets, click 'Submit' and adjust the inclusion criteria to fit your research purpose. The library will be adjusted based on the input genes and inclusion criteria.
Visit on the web:
http://www.smallmoleculesuite.org/apps/LibraryR/
Or run locally in R:
shiny::runGitHub("sorgerlab/smallmoleculesuite", subdir = "LibraryR")Design/idea by Nienke Moret and Marc Hafner (HMS - LINCS data and signature generation center)
R code by Nienke Moret (HMS - LINCS data and signature generation center)
Shiny/R web application development by Nicholas Clark (U of Cincinnati - LINCS data coordination and integration center)
Supervision by Peter Sorger (HMS - LINCS data and signature generation center)
Icon design and development by Vasileios Stathias (U of Miami - LINCS data coordination and integration center)
This work was supported by NIH grants U54-HL127365, U24-DK116204 and U54-HL127624.
LINCS data portal - A unified resource for accessing all LINCS dataset packages and entities.
http://lincsportal.ccs.miami.edu/dcic-portal/
iLINCS - A data analysis platform aimed at developing statistical methods and computational tools for integrative analysis of the data produced by the LINCS program.
http://www.ilincs.org/
HMS-LINCS Small Molecule Library
http://lincs.hms.harvard.edu/db/sm/
NIH LINCS Consortium
http://www.lincsproject.org/
HMS LINCS Center
http://lincs.hms.harvard.edu/
LINCS Data and Signature Generation Centers (DSGCs)
http://www.lincsproject.org/LINCS/centers/data-and-signature-generating-centers
BD2K-LINCS Data Coordination and Integration Center (DCIC)
http://lincs-dcic.org/
Laboratory of Systems Pharmacology (LSP)
http://hits.harvard.edu/the-program/laboratory-of-systems-pharmacology/
Sorger Lab
http://sorger.med.harvard.edu/
Laboratory for Statistical Genomics and Systems Biology
http://eh3.uc.edu/