ArticleJournal of cheminformatics2015
Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.
Article in Journal of cheminformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 53 citations in OpenAlex.
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- Latin American databases of natural products: biodiversity and drug discovery against SARS-CoV-2.RSC advances · 2021Article
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- Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.Briefings in bioinformatics · 2019Review
- The metaRbolomics Toolbox in Bioconductor and beyond.Metabolites · 2019Review
- chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models.Journal of cheminformatics · 2018Article
- Predicted Biological Activity of Purchasable Chemical Space.Journal of chemical information and modeling · 2018Article
- Melittin Constrains the Expression of Identified Key Genes Associated with Bladder Cancer.Journal of immunology research · 2018Article
- ChemSAR: an online pipelining platform for molecular SAR modeling.Journal of cheminformatics · 2017Article
- 3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine.Journal of chemical information and modeling · 2017Article
- Ezqsar: An R Package for Developing QSAR Models Directly From Structures.The open medicinal chemistry journal · 2017Article
- Open source molecular modeling.Journal of molecular graphics & modelling · 2016Review
- BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions.Journal of cheminformatics · 2016Article
- Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel.Bioinformatics (Oxford, England) · 2016Article
- Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling.Journal of cheminformatics · 2015Article
- Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules.Journal of cheminformatics · 2015Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process.
resultscamb 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).
conclusionsOverall, 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.