# Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods
## 3 Materials and Methods  
Several series of analogous compounds (123 derivatives) have been theoretically designed based on the structure of glutamic acid to build a compound library of glutamic acid derivatives. From simple structure groups to more complex molecules, the chemical structures of the compounds were designed using BIOVIA Draw 21.1. [166]. The number of 123 compounds was reached after analysing the structure of glutamic acid to make as many specific structural modifications as possible. The classes of compounds and the structural changes made to the fundamental molecule were selected following the information found in the scientific literature. Our purpose was initially to design as many structural derivatives as possible because, after characterising and selecting these compounds based on well-established steps, we would be left with as many derivatives with optimal properties as possible to study further. We also used the same software to generate the computational descriptors. To select suitable candidates for our purpose, we evaluated some properties of the molecules and their behaviour in the human organism. Physico-chemical characterisation of the desired compounds was carried out using SwissADME [92; 103] and MarvinSketch [105]. Water solubility was tested using AquaSol [90], Chemicalize [91] and SwissADME [167; 168]. Lipophilicity was analysed using SwissADME to determine the partition coefficients [169; 170; 171; 172; 173; 174]. Toxicity was assessed using Toxtree [93] by applying the Cramer rules and the Kroess and Verhaar scheme, and GUSAR [175] was used to evaluate the acute toxicity in rodents. Pharmacokinetic properties were analysed in terms of permeability and interactions with P-glycoprotein (P-gp) and some isoforms of cytochrome P450 using the SwissADME program. In addition, we evaluated the \"drug-likeness\" criteria according to [PERSON], [PERSON], Veber, [PERSON], and [PERSON] rules using MarvinSketch, Chemicalize and DruLiTo [97]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Affinity (kcal/mol)** & **Dist. from RMSD L. B** & **Dist. from RMSD U. B** \\ \hline
1 & \(-6.3\) & 0 & 0 \\
2 & \(-6\) & 1.805 & 4.092 \\
3 & \(-5.7\) & 2.296 & 2.819 \\
4 & \(-5.3\) & 4.405 & 5.497 \\
5 & \(-5.3\) & 9.763 & 11.591 \\
6 & \(-5.3\) & 2.674 & 3.792 \\
7 & \(-5.3\) & 3.029 & 5.193 \\
8 & \(-5.2\) & 2.142 & 2.893 \\
9 & \(-5.1\) & 2.042 & 2.793 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Molecular docking results for the 4 Db6 compound obtained using AutoDock Vina. Run time: 28.3 s.  
The metabolism of the compounds was assessed using Toxtree, SmartCyp [131], and SOMP [132] and the bioactivity was evaluated using Molinspiration [134] and SWISSTarget prediction [133] (to predict the most probable molecular targets). The anticarcinogenic effect was assessed with the CLC-Pred software (Version 2.0) [135], which estimates in silico the cytotoxic effect based on the structural formula; the mechanism of action and adverse/toxic effects were tested using PASsonline [137]. Molecular docking was performed using SwissDock [150], PatchDock Beta 1.3. [153; 176], AutoDockVina 1.1.2. [155], and UCSF Chimera 1.15 [177]; the similarity between compounds was evaluated using SwissSimilarity [178]. We assessed the irritant/corrosive effect on the skin and eyes, the effect on the reproductive system, biodegradability, and protein and DNA binding alerts using Toxtree and OSIRIS Property Explorer [94]. Considering all the computed properties and their biological potential, \"lead\" compounds were selected. We also attempted to validate our experimental procedures using positive and negative controls. Therefore, we chose methionine sulfoximine and phosphinothricin as positive controls for their proven activity of inhibiting glutamine synthetase [66; 149]. As a negative control, we initially thought of glutamic acid, being the parent molecule for our derivatives [179].