Predicting the membrane permeability of organic fluorescent probes by the deep neural network based lipophilicity descriptor DeepFl-LogP

Light microscopy has become an indispensable tool for the life sciences, as it enables the rapid acquisition of three-dimensional images from the interior of living cells/tissues. Over the last decades, super-resolution light microscopy techniques have been developed, which allow a resolution up to an order of magnitude higher than that of conventional light microscopy. Those techniques require labelling of cellular structures with fluorescent probes exhibiting specific properties, which are supplied from outside and therefore have to surpass cell membranes. Currently, major efforts are undertaken to develop probes which can surpass cell membranes and exhibit the photophysical properties required for super-resolution imaging. However, the process of probe development is still based on a tedious and time consuming manual screening. An accurate computer based model that enables the prediction of the cell permeability based on their chemical structure would therefore be an invaluable asset for the development of fluorescent probes. Unfortunately, current models, which are based on multiple molecular descriptors, are not well suited for this task as they require high effort in the usage and exhibit moderate accuracy in their prediction. Here, we present a novel fragment based lipophilicity descriptor DeepFL-LogP, which was developed on the basis of a deep neural network. DeepFL-LogP exhibits excellent correlation with the experimental partition coefficient reference data (R2 = 0.892 and MSE = 0.359) of drug-like substances. Further a simple threshold permeability model on the basis of this descriptor allows to categorize the permeability of fluorescent probes with 96% accuracy. This novel descriptor is expected to largely simplify and speed up the development process for novel cell permeable fluorophores.