Several small-molecule CDK inhibitors have been identified, but none have been approved for clinical use in the past few years. A new series of 4-[(3-hydroxybenzylamino)-methylene]-4H-isoquinoline-1,3-diones were reported as highly potent and selective CDK4 inhibitors. In order to find more potent CDK4 inhibitors, the interactions between these novel isoquinoline-1,3-diones and cyclin-dependent kinase 4 was explored via in silico methodologies such as 3D-QSAR and docking on eighty-one compounds displaying potent selective activities against cyclin-dependent kinase 4. Internal and external cross-validation techniques were investigated as well as region focusing, bootstraping and leave-group-out. A training set of 66 compounds gave the satisfactory CoMFA model (q² = 0.695, r² = 0.947) and CoMSIA model (q² = 0.641, r² = 0.933). The remaining 15 compounds as a test set also gave good external predictive abilities with r²_pred values of 0.875 and 0.769 for CoMFA and CoMSIA, respectively. The 3D-QSAR models generated here predicted that all five parameters are important for activity toward CDK4. Surflex-dock results, coincident with CoMFA/CoMSIA contour maps, gave the path for binding mode exploration between the inhibitors and CDK4 protein. Based on the QSAR and docking models, twenty new potent molecules have been designed and predicted better than the most active compound 12 in the literatures. The QSAR, docking and interactions analysis expand the structure-activity relationships of constrained isoquinoline-1,3-diones and contribute towards the development of more active CDK4 subtype-selective inhibitors.

Topoisomerase I is important for DNA replication and cell division, making it an attractive drug target for anticancer therapy. A series of indenoisoquinolines displaying potent Top1 inhibitory activity in human renal cell carcinoma cell line SN12C were selected to establish 3D-QSAR models using CoMFA and CoMSIA methods. Internal and external cross-validation techniques were investigated, as well as some measures taken, including region focusing, bootstrapping and the “leave-group-out” cross-validation method. The satisfactory CoMFA model predicted a q² value of 0.659 and an r² value of 0.949, indicating that electrostatic and steric properties play a significant role in potency. The best CoMSIA model, based on a combination of steric, electrostatic and H-bond acceptor descriptors, predicted a q² value of 0.523 and an r² value of 0.902. The models were graphically interpreted by contour plots which provided insight into the structural requirements for increasing the activity of a compound, providing a solid basis for future rational design of more active anticancer agents.

Selective S1P₁ receptor agonists have therapeutic potential to treat a variety of immune-mediated diseases. A series of 2-imino-thiazolidin-4-one derivatives displaying potent S1P₁ receptor agonistic activity were selected to establish 3D-QSAR models using CoMFA and CoMSIA methods. Internal and external cross-validation techniques were investigated as well as some measures including region focusing, progressive scrambling, bootstraping and leave-group-out. The satisfactory CoMFA model predicted a q² value of 0.751 and an r² value of 0.973, indicating that electrostatic and steric properties play a significant role in potency. The best CoMSIA model, based on a combination of steric, electrostatic, hydrophobic and H-bond donor descriptors, predicted a q² value of 0.739 and an r² value of 0.923. The models were graphically interpreted using contour plots which gave more insight into the structural requirements for increasing the activity of a compound, providing a solid basis for future rational design of more active S1P₁ receptor agonists.