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Molecular fingerprints are the workhorse in ligand-based drug discovery. Inrecent years, an increasing number of research papers reported fascinatingresults on using deep neural networks to learn 2D molecular representations asfingerprints. It is anticipated that the integration of deep learning wouldalso contribute to the prosperity of 3D fingerprints. Here, we unprecedentedlyintroduce deep learning into 3D small molecule fingerprints, presenting a newone we termed as the three-dimensional force fields fingerprint (TF3P). TF3P islearned by a deep capsular network whose training is in no need of labeleddatasets for specific predictive tasks. TF3P can encode the 3D force fieldsinformation of molecules and demonstrates the stronger ability to capture 3Dstructural changes, to recognize molecules alike in 3D but not in 2D, and toidentify similar targets inaccessible by other 2D or 3D fingerprints based ononly ligands similarity. Furthermore, TF3P is compatible with both statisticalmodels (e.g. similarity ensemble approach) and machine learning models.Altogether, we report TF3P as a new 3D small molecule fingerprint with apromising future in ligand-based drug discovery. All codes are written inPython and available at https://github.com/canisw/tf3p.

TF3P: Three-dimensional Force Fields Fingerprint Learned by Deep Capsular Network