  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.
