AZOrange – High performance open source machine learning for QSAR modeling in a graphical programming environment Jonna C Stalring, Lars A Carlsson, Pedro Almeida and Scott Boyer. Journal of Cheminformatics 2011, 3:28doi:10.1186/1758-2946-3-28
Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community.
Project homepage: AZOrange (Ubuntu, I assume it compiles and runs on other *nix platforms. I run Ubuntu so I need to setup another *nix distribution just for test purposes.)