Mmff94

The molecular model of the new thiourea derivatives was done using MOE software suite 10.2008. Following geometry optimization, a systematic conformational search was carried out to RMS gradient of 0.05 Å with energy minimization of the resultant conformations employing the ConfSearch module implemented in MOE. All molecular mechanics computations were performed with the Merck Force Field (MMFF94s). The crystallographic structure of M. tuberculosis enoyl reductase InhA in complex with N-{1-[(2-chloro-6-fluorophenyl)methyl]-1H-pyrazol-3-yl]-5-[(1S)-1-(3-methyl-1H-pyrazol-1-yl)ethyl]-1,3,4-thiadiazol-2-amine (GSK 625) was obtained from the Protein Data Bank (PDB ID: 5JFO). Water molecules were ignored and hydrogen atoms were added to the enzyme and partial charges were calculated. Validation followed by docking of the compounds into the active site were carried out, after removing the co-crystallized ligand. The target protein was kept rigid, while the ligands adopt 50 separate docking simulations using default parameters. The conformations were chosen based on their S score, and appropriate fitting with the relevant amino acids in the binding pocket.

As per proposed concept, the primary requirement is molecules already in the market, well profiled, and may be used for patient treatments. Therefore, we collected approved and well-known agonists of the proposed receptor CXCR4 along with the IC₅₀ values from the literature. Molecules were collected and compiled, and three-dimensional (3D) structures were generated using openbabel (OB)[56 (link)], and energy optimization (obminimize, an OB module) was used with the steepest descent method for 500 steps using the Merck molecular force field, MMFF94s[57 (link)]. The molecules were stored in.mol2 format, and the overall dataset resulted in 175 molecules (Additional file 6: Table S1), which were referred to as the training dataset. The processed molecules were labeled as inhibitors (1 or positive) if IC₅₀ < 0.05 µM, and the remaining were non-inhibitors (0 or negative), resulting in a dataset consisting of 81 inhibitors and 94 non-inhibitors (Additional file 6: Table S1). We prepared an independent evaluation dataset, retrieved from a directory of useful decoys-enhanced (DUD-E)[58 (link)], comprises 56 molecules, specifically for the CXCR4 receptor, classified as per their IC₅₀ value out of which 43 were inhibitors (1/active) and 13 non-inhibitors (0/decoy). The evaluation dataset was prepared as per the training dataset (Table 1).

The two-dimensional structures of synthesized SOX derivatives were drawn with the help of ChemDraw Professional v15.1 and subjected to prepare a three-dimensional structure with the help of Chem3D v15.1. The structural modification, geometrical correction, and optimization were performed with the help of Merck Molecular Force Field (MMFF94). Protoss is an online tool that automatically predicts hydrogens for the interaction between protein-ligand complexes (https://proteins.plus/), accessed on 5 February 2022. The substituted SOXs were subjected to a single-step minimization by the steepest descent method for 500 steps and an RMS gradient of 0.01 [85 (link)]. To perform docking analysis, the eutectic state of protonation of the ligands was found at pH 7.4.

An in silico analysis was performed to assess whether there is a role of serotonin 5-HT_1A inhibitory receptors in producing the antihyperalgesic and antiallodynic effects of amarisolide A. The crystal structure of the compound amarisolide A was obtained from the PubChem database. The structure was protonated using the Avogadro software V1.2.0 (Avogadro Chemistry, Pittsburgh, PA, USA) at pH 7.4, and the minimum energy spatial configuration was subsequently determined using the Merck Molecular Force Field (MMFF94, Merck Research Laboratories, Boston, MA, USA). The protein structure of serotonin 5-HT_1A receptor (7E2X) was obtained from the Protein Data Bank (PDB, https://www.rcsb.org/ (accessed on 14 November 2022)), selecting a resolution of less than 3 Å. Then, the docking was performed with the CB-Dock tool [47 (link)]. The results of the CB-Dock tool software V2.0 (Structural Bioinformatics Research group, Chengdu, China) were contrasted with UCSF Chimera 1.16 (Resource for Biocomputing, Visualization, and Informatics University of California, San Francisco, CA, USA) to protein preparation and Autodock vina 1.1.2 (Oleg Trott, La Jolla, CA, USA).

The Merck Molecular Force Field, MMFF94 [38 (link)] was used to evaluate the energy of a given molecule as implemented in Open Babel 2.4.1 [33 (link)]. This is an approximation of the molecule’s actual energy landscape; ideally, we would use quantum chemical methods to compute the molecule’s energy as accurately as possible [39 (link)].