Anonymous Pattern Molecular Fingerprint and its Applications on Property Identification

TL;DR

This paper introduces a novel molecular fingerprint called Anonymous-FP, which encodes atom chains in molecules using NLP techniques, significantly improving property identification accuracy across various datasets.

Contribution

The paper proposes a new anonymous molecular fingerprint method that captures multi-scale interactions and applies NLP encoding, enhancing molecular property prediction performance.

Findings

Anonymous-FP achieves over 93% accuracy on NCI datasets.

The atom chain scale r=8 optimizes representation quality.

Anonymous-FP outperforms traditional fingerprints in property identification.

Abstract

Molecular fingerprints are significant cheminformatics tools to map molecules into vectorial space according to their characteristics in diverse functional groups, atom sequences, and other topological structures. In this paper, we set out to investigate a novel molecular fingerprint \emph{Anonymous-FP} that possesses abundant perception about the underlying interactions shaped in small, medium, and large molecular scale links. In detail, the possible inherent atom chains are sampled from each molecule and are extended in a certain anonymous pattern. After that, the molecular fingerprint \emph{Anonymous-FP} is encoded in virtue of the Natural Language Processing technique \emph{PV-DBOW}. \emph{Anonymous-FP} is studied on molecular property identification and has shown valuable advantages such as rich information content, high experimental performance, and full structural significance. During the experimental verification, the scale of the atom chain or its anonymous manner matters significantly to the overall representation ability of \emph{Anonymous-FP}. Generally, the typical scale $r = 8$ enhances the performance on a series of real-world molecules, and specifically, the accuracy could level up to above $93\%$ on all NCI datasets.