ChemSAR: an online pipelining platform for molecular SAR modeling

Abstract

BackgroundIn recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers.ResultsThis work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files.ConclusionChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.Graphical abstract. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0215-1) contains supplementary material, which is available to authorized users.

Towards reproducible computational drug discovery

“…Kausar and Falcao [259] presents an automated framework based on KNIME for QSAR modeling entailing data pre-processing, model building and validation. Dong et al [260] introduced an online platform for QSAR modeling known as ChemSAR that is capable of handling chemical structures, computing molecular descriptors, model building as well as producing result plots. Tsiliki et al [261] proposed an R package known as RRegrs for building multiple regression models using a pre-configured and customizable workflow.…”

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

“…Web servers such as ChemSAR (Dong et al, 2017b ) and ChemBench (Capuzzi et al, 2017 ) enable users to build custom models for particular use with machine learning methods and molecular descriptors. For chemists who have in-house data for some particular endpoints, it will be convenient to use these web servers to build predictive models to prioritize or substitute in vitro or in vivo tests.…”

Machine learning in materials science

“…High‐throughput density functional calculations for molecular property prediction are highly time‐consuming. As an alternative, machine learning is a feasible approach for the fast prediction of structures or properties of molecules, compounds and materials; in addition, it can realize high accuracy . ElemNet is a model that is based on a DNN that takes elements as input for predicting material properties .…”

“…As an alternative, machine learning is a feasible approach for the fast prediction of structures or properties of molecules, compounds and materials; in addition, it can realize high accuracy. 13,14,42,43,62,74,75,[119][120][121][122][123][124][125][126][127][128] ElemNet is a model that is based on a DNN that takes elements as input for predicting material properties. 42 It extracts the physical and chemical interactions and similarities between elements automatically and makes fast and precise predictions.…”