are located outside the applicability domain uses extrapolation, and so is less reliable (on average) than prediction within the applicability domain. the assessment of the reliability of qsar predictions remains a research topic. the qsar equations can be used to predict biological activities of newer molecules before their synthesis. examples of machine learning tools for qsar modeling include: s.no.namealgorithmsexternal link 1.rrf,svm, naïve bayesian, and ann"r: the r project for statistical computing". 2.libsvmsvm"libsvm -- a library for support vector machines". 3.orangerf, svm, and naïve bayesian"orange data mining". 4.rapidminersvm, rf, naïve bayes, dt, ann, and k-nn"rapidminer | #1 open source predictive analytics platform". 5.wekarf, svm, and naïve bayes"weka 3 - data mining with open source machine learning software in java". 6.knimedt, naïve bayes, and svm"knime | open for innovation". 7.azorangert, svm, ann, and rf"azcomptox/azorange: astrazeneca add-ons to orange". github. 2018-09-19. 8.tanagrasvm, rf, naïve bayes, and dt"tanagra - a free data mining software for teaching and research". archived from the original on 2017-12-19. retrieved 2016-03-24. 9.elkik-nn"elki data mining framework". archived from the original on 2016-11-19. 10.mallet"mallet homepage". 11.moa"moa massive online analysis | real time analytics for data streams". archived from the original on 2017-06-19. 12.deep chemlogistic regression, naive bayes, rf, ann, and others"deepchem". deepchem.io. retrieved 20 october 2017. 13.alvamodelregression (ols, pls, k-nn, svm and consensus) and classification (lda/qda, pls-da, k-nn, svm and consensus)"alvamodel: a software tool to create qsar/qspr models". alvascience.com.