A series of novel compounds bearing imidazo[2,1-b]thiazole scaffolds were designed and synthesized based on the optimization of the virtual screening hit compound N-(6-morpholinopyridin-3-yl)-2-(6-phenylimidazo[2,1-b]thiazol-3-yl)acetamide (5a), and tested for their cytotoxicity against human cancer cell lines, including HepG2 and MDA-MB-231. The results indicated that the compound 2-(6-(4-chlorophenyl)imidazo[2,1-b]thiazol-3-yl)-N-(6-(4-(4-methoxybenzyl)piperazin-1-yl)pyridin-3-yl)acetamide (5l), with slightly higher inhibition on VEGFR2 than 5a (5.72% and 3.76% inhibitory rate at 20 μM, respectively), was a potential inhibitor against MDA-MB-231 (IC₅₀ = 1.4 μM) compared with sorafenib (IC₅₀ = 5.2 μM), and showed more selectivity against MDA-MB-231 than HepG2 cell line (IC₅₀ = 22.6 μM).

A series of benzimidazole carboxamide derivatives have been synthesized and characterized by ¹H-NMR, ¹³C-NMR and HRMS. PARP inhibition assays and cellular proliferation assays have also been carried out. Compounds 5cj and 5cp exhibited potential anticancer activities with IC₅₀ values of about 4 nM against both PARP-1 and PARP-2, similar to the reference drug veliparib. The two compounds also displayed slightly better in vitro cytotoxicities against MDA-MB-436 and CAPAN-1 cell lines than veliparib and olaparib, with values of 17.4 µM and 11.4 µM, 19.8 µM and 15.5 µM, respectively. The structure-activity relationship based on molecular docking was discussed as well.

Background: Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival. It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. These successes and appearances of drug resistance in some patients have raised significant interest and efforts in discovering new Src inhibitors. Various in-silico methods have been used in some of these efforts. It is desirable to explore additional in-silico methods, particularly those capable of searching large compound libraries at high yields and reduced false-hit rates. Results: We evaluated support vector machines (SVM) as virtual screening tools for searching Src inhibitors from large compound libraries. SVM trained and tested by 1,703 inhibitors and 63,318 putative non-inhibitors correctly identified 93.53%~ 95.01% inhibitors and 99.81%~ 99.90% non-inhibitors in 5-fold cross validation studies. SVM trained by 1,703 inhibitors reported before 2011 and 63,318 putative non-inhibitors correctly identified 70.45% of the 44 inhibitors reported since 2011, and predicted as inhibitors 44,843 (0.33%) of 13.56M PubChem, 1,496 (0.89%) of 168 K MDDR, and 719 (7.73%) of 9,305 MDDR compounds similar to the known inhibitors. Conclusions: SVM showed comparable yield and reduced false hit rates in searching large compound libraries compared to the similarity-based and other machine-learning VS methods developed from the same set of training compounds and molecular descriptors. We tested three virtual hits of the same novel scaffold from in-house chemical libraries not reported as Src inhibitor, one of which showed moderate activity. SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates.