Mustafa Alhaji Isa
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Research Interests
Mustafa Alhaji Isa has a total of 46 co-authors. Here are the top co-authors by number of shared publications:
Mohammed Mustapha Mohammed
7 shared publicationsFalmata Audu Gubio
3 shared publicationsRita Singh Majumdar
Sharda University
3 shared publicationsShazia Haider
Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, Knowledge Park 3, 201306, India
3 shared publicationsAdam Mustapha
Department of Microbiology, Faculty of Science, University of Maiduguri, Maiduguri, Nigeria
2 shared publicationsIbrahim Alkali Allamin
Department of Microbiology, Faculty of Science, University of Maiduguri, Maiduguri, Nigeria
2 shared publications
Latest Publications
- Open Access
- 28 September 2026
- Published by Elsevier in In Silico Research in Biomedicine
- Vol. 2
Machine learning guided identification and molecular dynamics evaluation of candidate SARS-CoV-2 main protease inhibitors from Xeromphis nilotica stem bark phytochemicals
- 28 September 2026
- Published by Elsevier in In Silico Research in Biomedicine
- Vol. 2
Open AccessMachine learning guided identification and molecular dynamics evaluation of candidate SARS-CoV-2 main protease inhibitors from Xeromphis nilotica stem bark phytochemicals
- Open Access
- 16 February 2026
- Published by Elsevier in In Silico Research in Biomedicine
- Vol. 2
Molecular detection of blaTEM in multidrug-resistant E. coliand deep learning-assisted computational identification of phytochemical inhibitors from Detarium microcarpum
- 16 February 2026
- Published by Elsevier in In Silico Research in Biomedicine
- Vol. 2
Open AccessMolecular detection of blaTEM in multidrug-resistant E. coliand deep learning-assisted computational identification of phytochemical inhibitors from Detarium microcarpum
- Open Access
- 15 February 2026
- Published by Elsevier in In Silico Research in Biomedicine
- Vol. 2
Computational and deep learning-assisted multi-epitope vaccine design targeting the RNA-dependent RNA polymerase of Nipah virus
- 15 February 2026
- Published by Elsevier in In Silico Research in Biomedicine
- Vol. 2
Open AccessComputational and deep learning-assisted multi-epitope vaccine design targeting the RNA-dependent RNA polymerase of Nipah virus
- Open Access
- 12 December 2025
- Published by Elsevier in In Silico Research in Biomedicine
- Vol. 2
Uncovering Zika virus-specific neurodevelopmental disruptions through deep learning and transcriptomic profiling of human neural cells
- 12 December 2025
- Published by Elsevier in In Silico Research in Biomedicine
- Vol. 2
Open AccessUncovering Zika virus-specific neurodevelopmental disruptions through deep learning and transcriptomic profiling of human neural cells
- 23 November 2025
- Published by Elsevier in Computational Biology and Chemistry
- Vol. 120 (Pt 2) , 108797
Discovery of Mangifera indica-based natural inhibitors against TEM-1 β-lactamase from Escherichia coli using machine learning approaches
- 23 November 2025
- Published by Elsevier in Computational Biology and Chemistry
- Vol. 120 (Pt 2) , 108797
Discovery of Mangifera indica-based natural inhibitors against TEM-1 β-lactamase from Escherichia coli using machine learning approaches
- 4 November 2025
- Published by Springer Nature in Journal of Computer-Aided Molecular Design
- Vol. 39 (2) , 1-30
Design, synthesis, deep learning-guided prediction, and biological evaluation of novel pyridine-thiophene-based imine-benzalacetophenone hybrids as promising antimicrobial agent
Antimicrobial resistance (AMR) remains a global health crisis, necessitating the development of novel therapeutics against multidrug-resistant pathogens. In this study, ten (10) hybrid imine-benzalacetophenone derivatives (7a–7j), incorporating pyridine and thiophene scaffolds, were synthesized and structurally characterized using FTIR, 1H-NMR, LC–MS, and elemental analysis. In vitro, antimicrobial screening demonstrated that compounds 7c and 7j displayed consistent and potent activity across Gram-positive and Gram-negative bacterial strains and fungal pathogens, with compound 7c achieving MICs as low as 25 µg/mL. Compound 7c exhibited significant antitubercular activity with 96% inhibition at 25 µg/mL against Mycobacterium tuberculosis H37Rv. A deep learning-based QSAR model, developed using a fully connected feedforward neural network trained on molecular descriptors (MolWt, LogP, TPSA, H-bond donors/acceptors, etc.), yielded predicted pMIC values closely matching experimental trends. SHAP analysis confirmed the multivariate contribution of key descriptors, validating the model’s interpretability despite dataset constraints. SwissADME-based pharmacokinetic profiling confirmed high gastrointestinal absorption, low PAINS alerts, and compliance with Lipinski and Veber rules for drug-likeness. Compounds 7c and 7j demonstrated balanced lipophilicity, low skin permeability, and favourable ADMET characteristics, aligning with their firm biological profiles. Molecular docking showed strong binding affinities for 7c (− 11.55 kcal/mol with CYP51) and 7j (− 9.97 kcal/mol with InhA), with multiple hydrogen bonds and hydrophobic interactions at catalytically relevant sites. These interactions were consistent with observed antimicrobial profiles. These docking predictions were validated by 200 ns molecular dynamics simulations, which confirmed the structural stability of 7c and 7j in complex with CYP51, InhA, PBP2a, and DNA Gyrase B. RMSD and RMSF trajectories, indicated stable ligand retention and minimized flexibility at the binding interface, particularly for 7c with CYP51 and InhA, and for 7j with DNA Gyrase B. These results support 7c and 7j as promising lead candidates with dual antimicrobial potential, favourable drug-like properties, and broad-spectrum activity profiles.
- 4 November 2025
- Published by Springer Nature in Journal of Computer-Aided Molecular Design
- Vol. 39 (2) , 1-30
Design, synthesis, deep learning-guided prediction, and biological evaluation of novel pyridine-thiophene-based imine-benzalacetophenone hybrids as promising antimicrobial agent
Antimicrobial resistance (AMR) remains a global health crisis, necessitating the development of novel therapeutics against multidrug-resistant pathogens. In this study, ten (10) hybrid imine-benzalacetophenone derivatives (7a–7j), incorporating pyridine and thiophene scaffolds, were synthesized and structurally characterized using FTIR, 1H-NMR, LC–MS, and elemental analysis. In vitro, antimicrobial screening demonstrated that compounds 7c and 7j displayed consistent and potent activity across Gram-positive and Gram-negative bacterial strains and fungal pathogens, with compound 7c achieving MICs as low as 25 µg/mL. Compound 7c exhibited significant antitubercular activity with 96% inhibition at 25 µg/mL against Mycobacterium tuberculosis H37Rv. A deep learning-based QSAR model, developed using a fully connected feedforward neural network trained on molecular descriptors (MolWt, LogP, TPSA, H-bond donors/acceptors, etc.), yielded predicted pMIC values closely matching experimental trends. SHAP analysis confirmed the multivariate contribution of key descriptors, validating the model’s interpretability despite dataset constraints. SwissADME-based pharmacokinetic profiling confirmed high gastrointestinal absorption, low PAINS alerts, and compliance with Lipinski and Veber rules for drug-likeness. Compounds 7c and 7j demonstrated balanced lipophilicity, low skin permeability, and favourable ADMET characteristics, aligning with their firm biological profiles. Molecular docking showed strong binding affinities for 7c (− 11.55 kcal/mol with CYP51) and 7j (− 9.97 kcal/mol with InhA), with multiple hydrogen bonds and hydrophobic interactions at catalytically relevant sites. These interactions were consistent with observed antimicrobial profiles. These docking predictions were validated by 200 ns molecular dynamics simulations, which confirmed the structural stability of 7c and 7j in complex with CYP51, InhA, PBP2a, and DNA Gyrase B. RMSD and RMSF trajectories, indicated stable ligand retention and minimized flexibility at the binding interface, particularly for 7c with CYP51 and InhA, and for 7j with DNA Gyrase B. These results support 7c and 7j as promising lead candidates with dual antimicrobial potential, favourable drug-like properties, and broad-spectrum activity profiles.
- 21 March 2024
- Published by Springer Nature in Journal of Proteins and Proteomics
- Vol. 15 (2) , 159-173
Identification of potential inhibitors of shikimate kinase from Mycobacterium tuberculosis using in silico approach
- 21 March 2024
- Published by Springer Nature in Journal of Proteins and Proteomics
- Vol. 15 (2) , 159-173
Identification of potential inhibitors of shikimate kinase from Mycobacterium tuberculosis using in silico approach