Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors

In this work, ten molecular compounds were optimised using density functional theory (DFT) method via Spartan 14. The obtained descriptors were used to develop quantitative structural activities relationship (QSAR) model using Gretl and Matlab software and the similarity between predicted IC50 and observed IC50 was investigated. Also, docking study revealed the non-bonding interactions between the studied compounds and the receptor. The molecular interactions between the observed ligands and brain cancer protein (PDB ID: 1q7f) were investigated. Adsorption, distribution, metabolism, excretion and toxicity (ADMET) properties were also investigated.


Triazole Glioblastoma Inhibitors
In-silico DFT QSAR Docking ADMET a b s t r a c t In this work, ten molecular compounds were optimised using density functional theory (DFT) method via Spartan 14. The obtained descriptors were used to develop quantitative structural activities relationship (QSAR) model using Gretl and Matlab software and the similarity between predicted IC 50 and observed IC 50 was investigated. Also, docking study revealed the non-bonding interactions between the studied compounds and the receptor. The molecular interactions between the observed ligands and brain cancer protein (PDB ID: 1q7f) were investigated. Adsorption, distribution, metabolism, excretion and toxicity (ADMET) properties were also investigated.

Value of the Data
• The data obtained from investigated triazole derivatives in this research will assist scientists to know the molecular descriptors that describe its anti-glioblastoma activity. • Data in this research will disclose the role of individual molecular descriptors obtained from optimised compounds in the developed QSAR model. • The obtained binding affinity will reveal the ability of each compound to inhibit brain tumor protein (PDB ID: 1q7f). • ADMET properties of the observed and proposed molecular compounds were also investigated in order to define the nature of triazole derivatives in receptor.

Data Description
The 2D structures of the molecules used in this research were shown in Table 1 . The observed compounds used in this work were obtained from the research carried out by Ewa et al., (2018) [1] . The compounds with inhibition concentration (IC 50 ) of ≤10 μM were selected and subjected to quantum chemical calculation using density functional theory via B3LYP (6-31G * basis set).
Thirteen descriptors ( Table 2 ) which describe anti-glioblastoma activities of the investigated triazole derivative were obtained and they were used for further research. The descriptors obtained were highest occupied molecular orbital energy (E HOMO ), lowest unoccupied molecular orbital (E LUMO ), band gap (BG), molecular weight (MW), area, volume, polar surface area (PSA), ovality, dipole moment (DM), log P, polarisability (POL), hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA). Table 3 revealed the developed QSAR model (which help to probe into biological activities of triazoles derivatives) from the calculated molecular descriptor obtained from Table 1 The Schematic diagram of the observed Triazole derivatives [1] .

SN
Molecular Structures IUPAC Name ( continued on next page )    Table 4 . Table 5 showed the calculated inhibition concentration (IC 50 ) for the investigated molecular compounds. The correlation between the predicted inhibition efficiency (IC 50 ) and observed efficiency (IC 50 ) were displayed in Fig. 1 . In this work, six (6) molecular compounds were proposed using the developed QSAR model and the inhibition concentration of individual proposed compound was predicted and displayed in Table 6 .  Also, Table 7 showed four molecular compounds ( 2, 7, 9 and 10) with −9.5 kcal/mol, −11.2 kcal/mol , −10.0 kcal/mol, and −9.4 kcal/mol respectively. The selected compounds were subjected to ADMET study using admetSAR server and the factor considered were based on adsorption, distribution, metabolism, excretion and toxicity of the investigated ligands. The obtained ADMET values were compared to the standard compound used (Carmustine).

Design, Materials and Methods
The studied triazole derivatives ( Table 1 ) were drawn using ChemDraw Ultra 8.0 and were optimised using Spartan 14 [5] . The optimization was accomplished using B3LYP with 6-31 G * Table 7 Obtained calculated ADMET Properties.  Table 8 Scoring and residues involved in the interaction between the studied complex.

Scoring (kcal/mol) Residues involved in the interactions
Types of Non-bonding interaction involved as basis set which produce descriptors that were used for further investigation. The selected calculated descriptors obtained from the optimised compounds were used to build robust QSAR model in order to relate the biological activity of the studied compounds to the calculated molecular parameters [6] . This was achieved using mathematical methods (multiple linear regression method) via Gretl 1.9.8. The observed inhibition concentration (IC 50 ) served as dependent variable while the calculated descriptors served as independent variables; thus, the QSAR model was developed. Several factors such as correlation coefficient (R 2 ), P-Value, F-value were considered to know the level of efficiency of the developed QSAR model. More so, validation of the developed QSAR model was implemented by observing some mathematical factors (cross validation correlation coefficient (C.V R 2 ), adjusted correlation coefficient) which could be calculated using Eq. (1) and 2 [7] .
Absorption, Distribution, Metabolism, Excretion and the Toxicity properties of the studied triazole derivatives were done via online software (admetSAR) (http://lmmd.ecust.edu.cn/admetsar1) [8] . The factors considered were Blood Brain Barrier, Caco-2 cell permeability, Human Intestinal Absorption, Ames test. Also, four software (Pymol (for treating downloaded protein), Autodock Tool (for locating binding site in the downloaded protein and for converting ligand and receptor to.pdbqt format from.pdb format), Auto dock vina (for docking calculation) and discovery studio (for viewing the non-bonding interaction between the docked complexes) were used to accomplish docking study between triazole derivative and brain tumor protein (PDB ID: 1q7f). The observed grid box was as follows: center ( X = 12.534, Y = 23.847, Z = 40.848) and size ( X = 68, Y = 64, Z = 72) as well as the spacing was set to be 1.00 Å .

Ethics Statement
Not Applicable.

Declaration of Competing Interest
The authors declare that they have no conflict of interest.