Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ

Polo-like kinase 1 (PLK1) and p38 gamma mitogen-activated protein kinase (p38 gamma) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38 gamma on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38 gamma. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38 gamma activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.

Automated summary

In this study, a combination strategy of deep-learning-based QSAR modeling and hybrid-based consensus scoring was used to screen for potential inhibitors against PLK1 and p38 gamma. Compound 4 was identified as a potent PLK1 inhibitor that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Compound 14 was found to inhibit both PLK1 and p38 gamma activities and showed promising inhibitory effects on human hepatocellular carcinoma and hepatoblastoma cells in vitro.