No abstract

Acute toxicity is one of the most challenging properties to predict purely with computational methods due to its direct relationship to biological interactions. Moreover, toxicity can be represented by different endpoints: it can be measured for different species using different types of administration, etc., and it is questionable if the knowledge transfer between endpoints is possible. We performed a comparative study of prediction multi-task toxicity for a broad chemical space using different descriptors and modeling algorithms and applied multi-task learning for a large toxicity dataset extracted from the Registry of Toxic Effects of Chemical Substances (RTECS). We demonstrated that multi-task modeling provides significant improvement over singleoutput models and other machine learning methods. Our research reveals that multi-task learning can be very useful to improve the quality of acute toxicity modeling and raises a discussion about the usage of multi-task approaches for regulation purposes.

In this work, we present graph-convolutional neural networks for the prediction of binding constants of protein−ligand complexes. We derived the model using multi task learning, where the target variables are the dissociation constant (K d ), inhibition constant (K i ), and half maximal inhibitory concentration (IC 50 ). Being rigorously trained on the PDBbind dataset, the model achieves the Pearson correlation coefficient of 0.87 and the RMSE value of 1.05 in pK units, outperforming recently developed 3D convolutional neural network model K deep .

Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting different ADMET and biological properties of molecules, which are frequently strongly correlated with one another. Such joint data analyses can increase the accuracy of models by exploiting their common representation and identifying common features between individual properties. In this work we review the recent developments in multi‐learning approaches as well as cover the freely available tools and packages that can be used to perform such studies.