Abstract

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.

Highlights

  • Small-molecule fluorescent chemical probes are important tools for bioimaging applications

  • These, so called, nanoscopes are integrated into fully automated platforms, which is important for high-throughput screening (HTS) applications in drug discovery and toxicity ­research[4]

  • Identification of cell permeable probes within a large set of available regular fluorophores is nowadays still based on a trial and error approach that involves screening hundreds of compounds (Fig. 1B), as the final probe should exhibit excellent cell permeability and specific binding to cellular targets (Fig. 1C), HTS synthesis platforms can speed up this process but are tedious and costly

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Summary

Introduction

Small-molecule fluorescent chemical probes are important tools for bioimaging applications. The accuracy of the algorithms used to calculate these descriptors is crucial for the reliability of these models, and for the precision of the prioritization tools When it comes to cell permeability the LogP descriptor is the most significant d­ escriptor[8,9,10,11]. Fragment descriptors (e.g. miLogP, Molinspiration) take the nuances of electronic or intramolecular interactions into account, which is not the case for atomistic algorithms They tend to perform better for larger molecules or compounds with more complex chemical structures, like fluorescent chemical probes. Another class of algorithms, e.g. M­ LogP19, calculate LogP by using molecular properties such as 3-D structures or topological indices. The rapid development of machine learning, e.g. artificial neural networks, has increased the accuracy and speed of many of these LogP ­descriptors[22]

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