{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T18:14:23Z","timestamp":1788372863598,"version":"build-2803163510"},"reference-count":99,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Acute Myeloid Leukemia (AML) is a hematological malignancy characterized by abnormal myeloid cell differentiation, disrupting normal hematopoiesis. This study utilized an integrated computational approach to identify key molecular targets and potential inhibitors for AML therapy. Analysis of the GSE9476 microarray dataset revealed 566 differentially expressed genes (DEGs), including 437 upregulated and 129 downregulated genes. Functional enrichment indicated significant roles in immune response, apoptosis regulation, and hematopoietic pathways. Protein-protein interaction (PPI) network analysis identified ten hub genes\u2014CD44, TNF, IL1B, MYC, STAT1, LCK, CCR7, FCGR3B, MMP9, and CD28\u2014implicated in AML pathogenesis. After examining these ten hub genes\u2019 resolution, R-factor, Diffraction Component Precision Index (DPI), least overfitting criteria, and other crystallographic features, we concluded that LCK and MMP9 were the best options for our future research. Pharmit was used to construct and validate an areceptor ligand pharmacophore model based on the crystal structures of LCK (PDB ID: 6PDJ) and MMP9 (PDB ID: 1GKC). Receptor-based pharmacophore models for LCK and MMP9 with AUC values of 0.79 and 0.85, respectively, were constructed and validated by virtual screening. The filtered compounds underwent ADME\/toxicity profiling before being precisely docked in the MMP9 and LCK, which was automated using Python scripts, in order to optimize the screening\u2019s dependability. Among all screened hits, favorable binding affinity values of \u22129.7 and \u22129.9 kcal\/mol for CID-56715853 and CID-126445459 with LCK and \u22128.9 and \u22129.2 kcal\/mol for MCULE-1367186142 and ZINC4896454 with MMP9, respectively, showed significant binding interactions. Molecular dynamics analyses, including RMSD, RMSF, PCA, and MM-GBSA, confirmed complex stability and binding affinity. This comprehensive in-silico framework integrates transcriptomic data, network analysis, pharmacophore modeling, and molecular simulations to identify novel biomarkers and drug candidates for AML, providing a foundation for further experimental validation and therapeutic development. These four hits (CID-56715853, CID-126445459 MCULE-1367186142 and ZINC4896454) are potential candidates for further in vitro and in vivo validation.<\/jats:p>","DOI":"10.1371\/journal.pone.0354456","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T17:28:02Z","timestamp":1788370082000},"page":"e0354456","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Acute Myeloid Leukemia(AML) proteins for in silico drug design using pharmacophore modeling, molecular docking and molecular dynamics simulation"],"prefix":"10.1371","volume":"21","author":[{"given":"Partho","family":"Bosu","sequence":"first","affiliation":[{"name":"National Institute of Technology","place":["Durgapur, West Bengal, India"],"department":["Department of Mathematics"]},{"name":"Jashore University of Science and Technology","place":["Jashore, Bangladesh"],"department":["Department of 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