VEGF is an important signaling protein involved in both vasculogenesis and angiogenesis. As an essential receptor protein tyrosine kinase propagating cellular signal transduction processes, VEGFR-2 is a central target for drug discovery against tumor-associated angiogenesis. Since the autophosphorylation of VEGFR-2 represents a key step in this signal pathway that contributes to angiogenesis, the discovery of small molecule inhibitors that block this reaction has attracted great interest for novel drugs research and development. Advances in the understanding of catalytic cleft and the conformational changes of DFG motif have resulted in the development of small molecule inhibitors known as type I and type II. High-resolution crystal structures of various inhibitors in complex with the receptor offer an insight into the relationship among binding modes, inhibition mechanisms, activity, selectivity and resistance. To control selectivity, improve activity and introduce intellectual property novelty, the strategies for the further development are discussed through structural and conformational analysis in this review.

With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.

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Fibrotic diseases have become a major cause of death in the developed world. AdipoR1 agonists are potent inhibitors of fibrotic responses. Here, we focused on the in silico identification of novel AdipoR1 peptide agonists. A homology model was constructed to predict the 3D structure of AdipoR1. By docking to known active peptides, the putative active site of the model was further explored. A virtual screening study was then carried out with a set of manually designed peptides using molecular docking. Peptides with high docking scores were then evaluated for their anti-fibrotic properties. The data indicated that the novel peptide Pep70 significantly inhibited the proliferation of hepatic stellate cells (HSC) and NIH-3T3 cells (18.33% and 27.80%) and resulted in favouring cell-cycle arrest through increasing the accumulation of cells in the G0/G1 phase by 17.08% and 15.86%, thereby reducing the cell population in the G2/M phase by 11.25% and 15.95%, respectively. Additionally, Pep70 exhibited the most marked suppression on the expression of α-smooth muscle actin (α-SMA), collagen type I alpha1 (COL1A1) and TGF-β1. Therefore, the peptide Pep70 was ultimately identified as an inhibitor of fibrotic responses and as a potential AdipoR1 agonist.