ИСТИНА

While we have seen a tremendous growth in machine learning methods over the last two decades there is still no one fits all solution. The next era of cheminformatics and pharmaceutical research in general is focused on mining the heterogeneous big data, which is accumulating at ever growing pace, and this will likely use more sophisticated algorithms such as Deep Learning (DL). There has been increasing use of DL recently which has shown powerful advantages in learning from images and languages as well as many other areas. However the accessibly of this technique for cheminformatics is hindered as it is not available readily to non-experts. It was therefore our goal to develop a DL framework embedded into a general research data management platform (Open Science Data Repository) which can be used as an API, standalone tool or integrated in new software as an autonomous module. In this poster we will present results of comparing performance of classic machine learning methods (Naïve Bayes, logistic regression, Support Vector Machines etc.) with Deep Learning and will discuss challenges associated with Ddeep Learning Neural Networks (DNN). The DNN learning models of different complexity (up to 6 hidden layers) were built and tuned (different number of hidden units per layer, multiple activation functions, optimizers, drop out fraction, regularization parameters, and learning rate) using Keras (https://keras.io/) and Tensorflow (www.tensorflow.org) and applied to various use cases connected to prediction of physicochemical properties, ADME, toxicity and calculating properties of materials. It was also shown that using nVidia GPUs significantly accelerates calculations, although memory consumption puts some limits on performance and applicability of standard toolkits "as is".