Computational identification of Mcl-1 inhibitors through molecular docking, ADMET, QSAR, and molecular dynamics simulations: anti-leukemic phytocompounds

Acute myeloid leukemia (AML) is a diverse hematologic cancer characterized by numerous genetic alterations and abnormal signaling pathways. Recent advances have greatly enhanced our understanding of AML’s molecular development, leading to innovative targeted treatments. However, drug resistance and disease variability remain significant clinical hurdles. Myeloid cell leukemia-1 (Mcl-1), an anti-apoptotic protein, is a key survival factor in AML cells and a promising therapeutic target. Therefore, this study uses an integrated computational drug discovery approach to identify new Mcl-1 inhibitors from phytochemicals. A library of 63 plant bioactive compounds with known anticancer properties was docked against Mcl-1. ADMET screening for pharmacokinetics and toxicity profiling, machine learning-based bioactivity prediction, and molecular dynamics simulations were performed on six candidate compounds. Six compounds showed favorable binding energy comparable to the control (− 9.517 kcal/mol), with Okanin (− 9.636 kcal/mol) and Lanceolin (− 9.430 kcal/mol) being the most potent. Post-docking MMGBSA analysis yielded values of − 43.280 and − 49.640 kcal/mol, respectively. MMPBSA calculations after MD simulation showed free binding energies of − 23.57 ± 2.86 and − 28.45 ± 6.76 kcal/mol, respectively. Ultimately, all lead compounds demonstrated good drug-like and pharmacokinetic properties, along with some predicted biological activity. Okanin, Lanceolin, Luteolin, 8-Methoxybutin, 4-Hydroxycephalotaxine, and Leptosin are computationally identified as potential Mcl-1 inhibitors. However, further in vitro and in vivo studies are necessary to confirm their effectiveness in anti-leukemia therapies.