TF3P: Three-dimensional Force Fields Fingerprint Learned by Deep Capsular Network
This work addresses the need for better 3D molecular representations in ligand-based drug discovery, offering a novel fingerprint that could enhance drug screening and target identification.
The authors tackled the problem of representing 3D molecular structures for drug discovery by introducing TF3P, a new 3D fingerprint learned via a deep capsular network without labeled data, which demonstrated stronger ability to capture 3D structural changes and identify similar targets compared to existing 2D and 3D fingerprints.
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.