  Molecular fingerprints are the workhorse in ligand-based drug discovery. In
recent years, an increasing number of research papers reported fascinating
results on using deep neural networks to learn 2D molecular representations as
fingerprints. It is anticipated that the integration of deep learning would
also contribute to the prosperity of 3D fingerprints. Here, we unprecedentedly
introduce deep learning into 3D small molecule fingerprints, presenting a new
one we termed as the three-dimensional force fields fingerprint (TF3P). TF3P is
learned by a deep capsular network whose training is in no need of labeled
datasets for specific predictive tasks. TF3P can encode the 3D force fields
information of molecules and demonstrates the stronger ability to capture 3D
structural changes, to recognize molecules alike in 3D but not in 2D, and to
identify similar targets inaccessible by other 2D or 3D fingerprints based on
only ligands similarity. Furthermore, TF3P is compatible with both statistical
models (e.g. similarity ensemble approach) and machine learning models.
Altogether, we report TF3P as a new 3D small molecule fingerprint with a
promising future in ligand-based drug discovery. All codes are written in
Python and available at https://github.com/canisw/tf3p.
