Format

Send to

Choose Destination
J Cheminform. 2015 Aug 28;7:45. doi: 10.1186/s13321-015-0086-2. eCollection 2015.

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

Author information

1
Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK.
2
Unite de Bioinformatique Structurale, Structural Biology and Chemistry Department, Institut Pasteur and CNRS UMR 3825, 25-28, rue Dr. Roux, 75 724 Paris, France.
3
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB101SD UK.
4
Unilever Research, Port Sunlight Laboratory, Bebington, L63 3JW Wirral UK.
#
Contributed equally

Abstract

BACKGROUND:

In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process.

RESULTS:

camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2).

CONCLUSIONS:

Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package.

KEYWORDS:

Ensemble; Learning; PCM; Package; QSAR; QSPR; R; Workflow

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center