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

TL;DR

This paper introduces TF3P, a novel 3D small molecule fingerprint learned by a deep capsular network that encodes 3D force fields without needing labeled data, enhancing molecule and target recognition in drug discovery.

Contribution

The paper presents TF3P, the first 3D molecular fingerprint learned via deep capsular networks that captures 3D force fields without requiring labeled datasets.

Findings

TF3P effectively encodes 3D force fields of molecules.

TF3P outperforms traditional 2D and 3D fingerprints in recognizing molecular similarities.

TF3P is compatible with various statistical and machine learning models.

Abstract

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.