Beyond RDKit descriptors, GUIDEMOL implements direct representations of 3D molecular structures. These types of representations have recently gained attention for their processing by complex artificial intelligence algorithms with larger data sets. The representation of molecules in 3D grids of voxels encoding their atoms has been used to train deep learning neural networks. Electrostatic potentials generated by point charges localized at the atom centers -another way of representing the 3D structure of molecules -has long since been used to establish relationships with molecular properties, namely in 3D-QSAR, or to assess molecular similarity. Direct 3D representations can be used for data sets of pre-aligned structures (e.g. relative to a reference or after docking to a receptor) or can be processed by convolutional neural networks. GUIDEMOL enables the calculation of 3D grids representing molecules based on the electrostatic potential or voxel encoding, and their parameters can be specified by the user with the graphical interface.