An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches
Merging Density Functional Theory (DFT) with the Quantitative Structure-Activity Relationship (2D/3D-QSAR) modeling represents a promising avenue for exploring antibacterial activity and discovering potential drugs effective against both gram-positive and gram-negative microorganisms. In this study,...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
|
| Series: | Scientific African |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S246822762400022X |
| _version_ | 1827329762926788608 |
|---|---|
| author | Marwa Manachou Ossama Daoui Oussama Abchir Rahma Dahmani Souad Elkhattabi Abdelouahid Samadi Salah Belaidi Samir Chtita |
| author_facet | Marwa Manachou Ossama Daoui Oussama Abchir Rahma Dahmani Souad Elkhattabi Abdelouahid Samadi Salah Belaidi Samir Chtita |
| author_sort | Marwa Manachou |
| collection | DOAJ |
| description | Merging Density Functional Theory (DFT) with the Quantitative Structure-Activity Relationship (2D/3D-QSAR) modeling represents a promising avenue for exploring antibacterial activity and discovering potential drugs effective against both gram-positive and gram-negative microorganisms. In this study, we employed this integrated approach to investigate a newly synthesized and promising class of 1,3,4-oxadiazole derivatives renowned for their high performance as antibacterial agents. To ensure an accurate characterization and thorough description of the targeted biological activity, we systematically evaluated various DFT functionals to precisely predict the geometrical and electronic descriptors of the investigated compounds. These descriptors were crucial for developing and validating the proposed 2D/3D-QSAR models. Our results reveal that the introduction of a donor group enhances the antibacterial activity of the derivatives. The analysis of molecular descriptors underscores the positive impact of this modification on the compounds' efficacy against bacteria. Additionally, our experimental compounds exhibit favorable characteristics concerning oral bioavailability, a crucial aspect in drug development. The identification of robust correlations between antibacterial activity and specific descriptors was achieved by conducting an extensive analysis that encompassed multiple linear regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN). Subsequently, a partial least squares (PLS) model was employed to construct 3D-QSAR models based on the Comparative Molecular Field Analysis (CoMFA) and Comparative Similarity Indices Analysis (CoMSIA) descriptors. Validation of the output models was performed using leave-one-out and bootstrapping strategies. The resultant 2D/3D-QSAR models demonstrated a high correlation between experimental and predicted activity values. Leveraging these models, the developed MLR model, expressed as pMIC = -12.704 + 0.260 logP – 6.104 10-03 SAG – 51.385 qN33, serves as a valuable tool for predicting antibacterial activity. we indicate that the machine learning methods, Artificial Neural Networks (ANN) and Random Forest (RF), outperform traditional models in accurately predicting antibacterial activity. We designed ten novel molecules, subjecting them to molecular docking and molecular dynamics simulations to predict their optimal postures when docked in the target and gain insights into the formed interactions. Additionally, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis was applied to discern the potential behavior of these novel compounds in the human body. As a result, the newly suggested chemical, X4, X10, exhibited robust inhibitory potential against gram-negative microbes. |
| first_indexed | 2024-03-07T15:46:47Z |
| format | Article |
| id | doaj.art-b6557d26bc8f4ab880b72795395ae46f |
| institution | Directory Open Access Journal |
| issn | 2468-2276 |
| language | English |
| last_indexed | 2024-03-07T15:46:47Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj.art-b6557d26bc8f4ab880b72795395ae46f2024-03-05T04:30:26ZengElsevierScientific African2468-22762024-03-0123e02078An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approachesMarwa Manachou0Ossama Daoui1Oussama Abchir2Rahma Dahmani3Souad Elkhattabi4Abdelouahid Samadi5Salah Belaidi6Samir Chtita7Faculty of Sciences of Tunis, Research Laboratory Characterization, Applications and Modeling of Materials, LCAMM, University of Tunis El Manar, Tunis 2092, TunisiaLaboratory of Engineering, Systems, and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Fez BP 72, MoroccoLaboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca B. P 7955, MoroccoFaculty of Sciences of Tunis, Research Laboratory Characterization, Applications and Modeling of Materials, LCAMM, University of Tunis El Manar, Tunis 2092, TunisiaLaboratory of Engineering, Systems, and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Fez BP 72, MoroccoDepartment of Chemistry, College of Science, UAEU, P.O. Box No. 15551, Al Ain, UAE; Corresponding authors.Department of Chemistry, Faculty of Exact Sciences, Group of Computational and Medicinal Chemistry, LMC E Laboratory, Biskra University, Biskra 07000, AlgeriaLaboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca B. P 7955, Morocco; Corresponding authors.Merging Density Functional Theory (DFT) with the Quantitative Structure-Activity Relationship (2D/3D-QSAR) modeling represents a promising avenue for exploring antibacterial activity and discovering potential drugs effective against both gram-positive and gram-negative microorganisms. In this study, we employed this integrated approach to investigate a newly synthesized and promising class of 1,3,4-oxadiazole derivatives renowned for their high performance as antibacterial agents. To ensure an accurate characterization and thorough description of the targeted biological activity, we systematically evaluated various DFT functionals to precisely predict the geometrical and electronic descriptors of the investigated compounds. These descriptors were crucial for developing and validating the proposed 2D/3D-QSAR models. Our results reveal that the introduction of a donor group enhances the antibacterial activity of the derivatives. The analysis of molecular descriptors underscores the positive impact of this modification on the compounds' efficacy against bacteria. Additionally, our experimental compounds exhibit favorable characteristics concerning oral bioavailability, a crucial aspect in drug development. The identification of robust correlations between antibacterial activity and specific descriptors was achieved by conducting an extensive analysis that encompassed multiple linear regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN). Subsequently, a partial least squares (PLS) model was employed to construct 3D-QSAR models based on the Comparative Molecular Field Analysis (CoMFA) and Comparative Similarity Indices Analysis (CoMSIA) descriptors. Validation of the output models was performed using leave-one-out and bootstrapping strategies. The resultant 2D/3D-QSAR models demonstrated a high correlation between experimental and predicted activity values. Leveraging these models, the developed MLR model, expressed as pMIC = -12.704 + 0.260 logP – 6.104 10-03 SAG – 51.385 qN33, serves as a valuable tool for predicting antibacterial activity. we indicate that the machine learning methods, Artificial Neural Networks (ANN) and Random Forest (RF), outperform traditional models in accurately predicting antibacterial activity. We designed ten novel molecules, subjecting them to molecular docking and molecular dynamics simulations to predict their optimal postures when docked in the target and gain insights into the formed interactions. Additionally, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis was applied to discern the potential behavior of these novel compounds in the human body. As a result, the newly suggested chemical, X4, X10, exhibited robust inhibitory potential against gram-negative microbes.http://www.sciencedirect.com/science/article/pii/S246822762400022XAntimicrobial evaluationComparative molecular field analysisComparative similarity indices analysisDensity functional theoryMolecular dockingMolecular dynamics |
| spellingShingle | Marwa Manachou Ossama Daoui Oussama Abchir Rahma Dahmani Souad Elkhattabi Abdelouahid Samadi Salah Belaidi Samir Chtita An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches Scientific African Antimicrobial evaluation Comparative molecular field analysis Comparative similarity indices analysis Density functional theory Molecular docking Molecular dynamics |
| title | An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches |
| title_full | An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches |
| title_fullStr | An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches |
| title_full_unstemmed | An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches |
| title_short | An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches |
| title_sort | antibacterial lead identification of novel 1 3 4 oxadiazole derivatives based on molecular computer aided design approaches |
| topic | Antimicrobial evaluation Comparative molecular field analysis Comparative similarity indices analysis Density functional theory Molecular docking Molecular dynamics |
| url | http://www.sciencedirect.com/science/article/pii/S246822762400022X |
| work_keys_str_mv | AT marwamanachou anantibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT ossamadaoui anantibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT oussamaabchir anantibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT rahmadahmani anantibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT souadelkhattabi anantibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT abdelouahidsamadi anantibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT salahbelaidi anantibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT samirchtita anantibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT marwamanachou antibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT ossamadaoui antibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT oussamaabchir antibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT rahmadahmani antibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT souadelkhattabi antibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT abdelouahidsamadi antibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT salahbelaidi antibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches AT samirchtita antibacterialleadidentificationofnovel134oxadiazolederivativesbasedonmolecularcomputeraideddesignapproaches |