g protein - coupled receptors ( gpcrs ) are regarded to take up more than one - fourth of marketed human medicines [ 1 , 2 ] . drugs targeting gpcrs account for the majority of the best - selling drugs and about 40% of all prescription pharmaceuticals in the marketplace . histamine receptors belong to one family of rhodopsin - like class a gpcrs , and four subtypes are named in chronological order as h1 , h2 , h3 , and h4 . each type of histamine receptor reacts to subtype - specific ligands into an active or inactive form . gpcrs are integral membrane proteins that consist of 7 transmembrane segments connected by 3 intracellular and 3 extracellular loops of variable length . the crystal structures of gpcrs with their binding ligands have revealed the features of the ligand binding pockets and extracellular loops [ 4 - 6 ] . a ligand of gpcr activates the receptor by changing the receptor structure to the active form . 1 ) is produced by decarboxylation of l - histidine and acts as a chemical mediator and neurotransmitter in central and peripheral tissues . it acts as an important pharmacological modulator involved in the processes of allergy , inflammation , neurophysiology , and cancer [ 7 - 10 ] . the molecular understanding of ligand - receptor interactions of gpcr remains unclear and is still the subject of investigations . there are several theories explaining the ligand - receptor interaction mechanism , such as shape theory , binding theory , and vibration theory [ 11 , 12 ] . the structures of histamine receptor ligands are so variable that we can not easily classify the pharmacological function of the ligand . to find any other characteristic in the molecular patterns between agonists and antagonists of histamine receptors , a computational approach to molecular vibration was carried out in an attempt to find a bit of the molecular interaction mechanisms . the simplified molecular - input line - entry system ( smiles ) and 3-dimensional structure data format ( sdf ) files of the dataset were downloaded from the pubchem compound database in national center for biotechnology information ( ncbi ) and used in the further analyses . all 47 ligand molecules in the dataset , comprising 9 histamine agonists and 38 histamine antagonists , are shown in table 1 . the molecular similarities between histamine and other 46 chemicals were calculated from smiles of the chemicals . the structural similarity was calculated and represented as the tanimoto distance of each molecule from histamine . the tanimoto coefficient for pairwise comparison of molecules is the most widely used measure of molecular structural similarity . this coefficient is defined as tc = nab/(na + nb - nab ) , with nab being the number of common bits , na the unique bits in molecule a , and nb the unique bits in molecule b , using a molecular fingerprint . in this study , the molecular similarity was calculated as the tanimoto coefficient using the 38-bit set . in order to calculate molecular vibrational frequency since each provided theoretical 3-d conformer sdf is not at an energy minimum and may not represent the lowest energetic form in a vacuum , solvent , or a binding pocket , each sdf file of a ligand molecule underwent conversion to a single low - energy conformation using the general atomic and molecular electronic structure system ( gamess ) program package . restricted hartree - fock ( rhf ) calculations using becke 's exchange and lee - yang - parr 's correlation functionals ( blyp ) density functional theory ( dft ) method with 6 - 31 g basis set were performed to optimize the geometries of the molecules . each result was taken as the representative conformation of the molecule , although the calculation of molecular vibrational frequency has some dependence on conformation . each geometry optimization result was subjected to the calculation step for the vibrational frequency with runtyp of hessian in the gamess program . for the simplified molecular comparison , the calculated vibrational frequencies of a molecule were then sorted in increasing order and taken into the corrals , the step size of which was 5 . the intensities of each frequency in the same corral were summed up as the representative of the corral in the frequency range of 0 - 5,000 cm . as a final outcome , this potential molecular descriptor of each molecule was displayed in a 1-dimensional vector containing 1,000 elements . finally , the similarity matrix , comprising the descriptors of 47 ligands of histamine receptor , was then subjected to hierarchical clustering in the agglomerative manner . in this study , the calculations of cimvf were performed by in - house scripts , written in python . the simplified molecular - input line - entry system ( smiles ) and 3-dimensional structure data format ( sdf ) files of the dataset were downloaded from the pubchem compound database in national center for biotechnology information ( ncbi ) and used in the further analyses . all 47 ligand molecules in the dataset , comprising 9 histamine agonists and 38 histamine antagonists , are shown in table 1 . the molecular similarities between histamine and other 46 chemicals were calculated from smiles of the chemicals . the structural similarity was calculated and represented as the tanimoto distance of each molecule from histamine . the tanimoto coefficient for pairwise comparison of molecules is the most widely used measure of molecular structural similarity . this coefficient is defined as tc = nab/(na + nb - nab ) , with nab being the number of common bits , na the unique bits in molecule a , and nb the unique bits in molecule b , using a molecular fingerprint . in this study , in order to calculate molecular vibrational frequency , the structure of a chemical must first be geometrically optimized . since each provided theoretical 3-d conformer sdf is not at an energy minimum and may not represent the lowest energetic form in a vacuum , solvent , or a binding pocket , each sdf file of a ligand molecule underwent conversion to a single low - energy conformation using the general atomic and molecular electronic structure system ( gamess ) program package . restricted hartree - fock ( rhf ) calculations using becke 's exchange and lee - yang - parr 's correlation functionals ( blyp ) density functional theory ( dft ) method with 6 - 31 g basis set were performed to optimize the geometries of the molecules . each result was taken as the representative conformation of the molecule , although the calculation of molecular vibrational frequency has some dependence on conformation . each geometry optimization result was subjected to the calculation step for the vibrational frequency with runtyp of hessian in the gamess program . for the simplified molecular comparison , the calculated vibrational frequencies of a molecule were then sorted in increasing order and taken into the corrals , the step size of which was 5 . the intensities of each frequency in the same corral were summed up as the representative of the corral in the frequency range of 0 - 5,000 cm . as a final outcome , this potential molecular descriptor of each molecule was displayed in a 1-dimensional vector containing 1,000 elements . finally , the similarity matrix , comprising the descriptors of 47 ligands of histamine receptor , was then subjected to hierarchical clustering in the agglomerative manner . in this study , the calculations of cimvf were performed by in - house scripts , written in python . typically , a tanimoto coefficient > 0.85 is considered highly similar , and a coefficient > 0.75 is considered similar for the purpose of clustering molecules that may have similar biological activity profiles . the highest tanimoto coefficient among agonists was 0.45 ( imetit ) , and this is not high enough to be considered a molecule that has potential agonist efficacy . moreover , the lowest value of tanimoto coefficient among agonists was 0.08 ( dimaprit and skf91488 ) and is also a lower value as an antagonist . it seems that there is no related pattern between tanimoto coefficients and the functional types of molecules in the case of histamine receptor agonists or antagonists . to search for a novel characteristic for the classification of histamine receptor ligands , a kind of molecular calculation using agglomerative hierarchical clustering was adopted in this work . the result of the hierarchical clustering of the similarity matrix from cimvf is shown in fig . as shown in the figure , eight agonists were located nearby ( the part in the dotted circle ) , except impromidine , and all antagonists were clustered close to each other in the radial tree . we can tell the regional difference between agonists and antagonists in the tree and also find that the information from the molecular vibrational frequency may play a role in the classification of agonists / antagonists for histamine receptor as a possible molecular descriptor . for these methods , clustering with cimvf shows the more proper result in the case of histamine receptor ligands . with a more concentrated study on the relationship between the molecular vibrational frequency and pharmacological function of a ligand , the vibrational spectrum of a molecule may shed light on the field of ligand - receptor interaction mechanisms .