  The novel coronavirus (2019-nCoV) has been declared to be a new international
health emergence and no specific drug has been yet identified. Several methods
are currently being evaluated such as protease and glycosylated spike (S)
protein inhibitors, that outlines the main fusion site among coronavirus and
host cells. Notwithstanding, the Heptad Repeat 1 (HR1) domain on the
glycosylated spike (S) protein is the region with less mutability and then the
most encouraging target for new inhibitors drugs.The novelty of the proposed
approach, compared to others, lies in a precise training of a deep neural
network toward the 2019-nCoV virus. Where a Siamese Neural Network (SNN) has
been trained to distingue the whole 2019-nCoV protein sequence amongst two
different viruses family such as HIV-1 and Ebola. In this way, the present deep
learning system has precise knowledge of peptide linkage among 2019-nCoV
protein structure and differently, of other works, is not trivially trained on
public datasets that have not been provided any ligand-peptide information for
2019-nCoV. Suddenly, the SNN shows a sensitivity of $83\%$ of peptide affinity
classification, where $3027$ peptides on SATPdb bank have been tested towards
the specific region HR1 of 2019-nCoV exhibiting an affinity of $93\%$ for the
peptidyl-prolyl cis-trans isomerase (PPIase) peptide. This affinity between
PPIase and HR1 can open new horizons of research since several scientific
papers have already shown that CsA immunosuppression drug, a main inhibitor of
PPIase, suppress the reproduction of different CoV virus included SARS-CoV and
MERS-CoV. Finally, to ensure the scientific reproducibility, code and data have
been made public at the following link: https://github.com/bionick87/2019-nCoV
