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The screenshot below displays RMSE values for 9 models developed for BCF (bio-concentration factor) endpoint based on the "BCF train" basket.
To summarize, the comprehensive modeling tool complemented with the multiple models overview page is a powerful framework for comprehensive QSAR research.
Comprehensive modeling can be a very powerful feature. Which descriptors are the best? How do the models evolve when outliers are excluded? Which training method performs best for this property? All this questions require deep analysis made possible using comprehensive modeling.
The screenshot below shows an intermediate result of a real on-going study – prediction of melting point based on more than 30,000 experimental measurements. More than 150 models have been built. Using the comprehensive modeling feature, it was possible to identify the best methods and to gradually improve models by removing outliers and reducing noise.
