Structure-based virtual screening and lead identification of novel protein kinase inhibitors through integrated computational approaches

Identification of novel PKR inhibitory chemotypes via integrated molecular modelling and machine learning

This work demonstrates that integrating docking-derived pharmacophores with conformational ensemble-based machine learning provides an effective approach for discovering novel inhibitors against underexplored kinase targets.

Computational and Structure-Guided E-Pharmacophore-Based Virtual Screening for the Identification of Novel NEK2 Kinase Inhibitors as Potential Anticancer Agents

Identification of novel EGFR inhibitors for glioblastoma through pharmacophore-guided virtual screening and molecular dynamics

The pharmacophore-based screening and docking analysis identified eleven promising EGFR-binding compounds, of which eight demonstrated optimal ADMET characteristics and stable interactions within the active site during molecular dynamics simulations, suggesting their potential efficacy as EGFR inhibitors.

Structure-Based Virtual Screening and De Novo Design Guided by 3D-QSAR Triage for the Identification of Nanomolar STK3 Inhibitors

Serine/threonine kinase 3 (STK3) is a core kinase in the Hippo signaling pathway that regulates cell proliferation, differentiation, and apoptosis. Dysregulation of this pathway is linked to various cancers and immune disorders, emphasizing STK3 as a promising therapeutic target for diseases that involve abnormal cell growth. Despite their therapeutic relevance, only a limited number of potent STK3 inhibitors have been reported, likely due to the difficulty in designing molecules that can bind tightly to the ATP-binding site of STK3. Using the 2-(2-amino-5-phenylpyrimidin-4-yl)-5-methoxyphenol (APPP) scaffold identified through virtual screening, we discovered a series of novel, potent STK3 inhibitors with half-maximal inhibitory concentration (IC50) values as low as 3.52 nM. This was made possible through an integrated molecular design strategy that combined structure-based de novo design with a predictive 3D-QSAR model to estimate IC50 values and prioritize compounds for synthesis. This approach improved the prioritization of compounds for synthesis by enhancing binding energy discrimination beyond the limitations of scoring functions in de novo design. Strategic derivatization at four defined positions on the APPP core enhanced interactions in the ATP-binding site of STK3, resulting in 18 of the 25 synthesized derivatives exhibiting IC50 values below 100 nM. These findings are particularly significant given the scarcity of known STK3 inhibitors with comparable potency, while also indicating that 3D-QSAR modeling contributed as a complementary triage tool rather than a standalone driver of lead discovery. This advancement stems from the use of quantum-mechanically derived 3D structural alignments and the incorporation of electrostatic potential distributions as quantitative molecular descriptors. Taken together, this study establishes a robust design paradigm for the rapid development of highly potent kinase inhibitors through structure-based computational strategies.

Discovery of Potent CDK2 Inhibitors Through Structure-Based Virtual Screening and MD Simulation Studies.

Overall, MD1-MD5 demonstrated excellent binding, structural stability, and pharmacokinetic properties, making them strong candidates for future CDK2-targeted anticancer research.

Discovery of novel METTL3 inhibitors: design, synthesis, and in silico and in vitro evaluations

Methyltransferase-like protein 3 (METTL3) is a promising therapeutic target for Acute Myeloid Leukemia (AML). To discover novel METTL3 inhibitors, we designed a series of compounds by hybridizing the natural product Genistein and the non-nucleoside METTL3 inhibitor STM2457. Among the 31 de novo-generated compounds subjected to molecular docking, the top-scoring hit H1 was selected for chemical synthesis and its structure was confirmed by NMR and MS. H1 showed an inhibitory activity against METTL3 with an IC50 value of 980.3 nM, significantly better than Genistein (37.31% inhibition at 40 µM). Molecular docking revealed that H1's binding mode was similar to STM2457, forming hydrogen bonds and hydrophobic interactions within the METTL3 pocket. Druglikeness prediction by SwissADMET and ADMETlab 3.0 showed that H1 exhibited to be within an acceptable range. This study validated our design strategy and provided a foundation for developing more potent and selective METTL3 inhibitors.