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Lead generation of UPPS inhibitors targeting MRSA: Using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamic simulations

• Authors : Basma M. Qandeel; Samar Mowafy; Khaled A. M. Abouzid

Subjects: Virtual screening; Binding affinities; Drug Target Identification

• Source: BMC ChemBMC Chemistry, Vol 18, Iss 1, Pp 1-22 (2024)

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A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis.

• Authors : Farooq, Fozia Bashir¹ (AUTHOR); Idrees, Nazeran² (AUTHOR) ; Noor, Esha² (AUTHOR)

• Source: BMC Chemistry. 1/7/2025, Vol. 19 Issue 1, p1-14. 14p.

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Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study.

• Authors : Pitakbut, Thanet^1,2 (AUTHOR); Munkert, Jennifer^1,3 (AUTHOR); Xi, Wenhui² (AUTHOR)

• Source: BMC Chemistry. 12/20/2024, Vol. 18 Issue 1, p1-16. 16p.

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Comparison of various methods for validity evaluation of QSAR models.

• Authors : Shayanfar, Shadi¹ (AUTHOR); Shayanfar, Ali^2,3 (AUTHOR)

• Source: BMC Chemistry. 8/23/2022, Vol. 16 Issue 1, p1-9. 9p.

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Computational approaches: atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted anti-tubercular agents

• Authors : Debadash Panigrahi; Susanta Kumar Sahu

Subjects: Atom based 3D-QSAR; Molecular docking; InhA inhibitor

• Source: BMC Chemistry, Vol 19, Iss 1, Pp 1-28 (2025)

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