ABSTRACT
Depression has been the number one disease, causing a high motility rate in the world, marking the need for new highly effective antidepressants that can alleviate the situation. Unfortunately, the price of carrying out experimentally the whole process of drug discovery and design costs an endeavor billion of money. Yet, computer-aided drug design is a cost-effective way of obtaining new potential drugs within a short period of time. A study helpful in designing potent drug candidate as an antidepressant with increased efficacy was carried out. The Density Functional Theory (DFT) method with B3LYP 6-311G++(d,p) basis set were employed using Gaussian 09 software to find the minimum energy configuration of the studied molecules (new derivatives of trazodone). Afterward, a molecular docking simulation of the studied molecules within the ligand pocket of the Human serotonin transporter as an antidepressant target followed. The molecular docking simulation was done using AutoDock Vina 1.1.2 software together with AutoDock tool 1.5.6 and PyMOL 1.7.4.5 software for the preparation of the docking molecules and the analysis of the results, respectively. From the analyzed molecular docking simulation results, it was demonstrated that all the proposed trazodone derivatives were revealed to be potential drug candidates with enhanced efficacy than the parent trazodone drug except for derivative ligand T2. Despite that from the analyzed DFT calculation results, the ligand T5, T7, T8, and T10 expressed significant chemical reactivity and thermodynamic properties in comparison with the trazodone. A further study on these derivatives as the potent antidepressant candidate is recommended.
ABDUL, A (2021). Assessment Of The Efficacy Of New Derivatives Of Trazodone As Antidepressants: A Computational Approach. Afribary. Retrieved from https://track.afribary.com/works/assessment-of-the-efficacy-of-new-derivatives-of-trazodone-as-antidepressants-a-computational-approach
ABDUL, ANIFA "Assessment Of The Efficacy Of New Derivatives Of Trazodone As Antidepressants: A Computational Approach" Afribary. Afribary, 23 Apr. 2021, https://track.afribary.com/works/assessment-of-the-efficacy-of-new-derivatives-of-trazodone-as-antidepressants-a-computational-approach. Accessed 23 Nov. 2024.
ABDUL, ANIFA . "Assessment Of The Efficacy Of New Derivatives Of Trazodone As Antidepressants: A Computational Approach". Afribary, Afribary, 23 Apr. 2021. Web. 23 Nov. 2024. < https://track.afribary.com/works/assessment-of-the-efficacy-of-new-derivatives-of-trazodone-as-antidepressants-a-computational-approach >.
ABDUL, ANIFA . "Assessment Of The Efficacy Of New Derivatives Of Trazodone As Antidepressants: A Computational Approach" Afribary (2021). Accessed November 23, 2024. https://track.afribary.com/works/assessment-of-the-efficacy-of-new-derivatives-of-trazodone-as-antidepressants-a-computational-approach