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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 abstract   From compounds and data to models: a complete model building workflow in one package.Electronic supplementary material  The online version of this article (doi:10.1186/s13321-015-0086-2) contains supplementary material, which is available to authorized users.

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