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

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 data sets 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.