Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.

Introduction Since December 2019, severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) has caused the coronavirus disease 2019 (COVID-19) pandemic in China and worldwide. New drugs for the treatment of COVID-19 are in urgent need. Considering the long development time for new drugs, the identification of promising inhibitors from FDA-approved drugs is an imperative and valuable strategy. Recent studies have shown that the S1 and S2 subunits of the spike protein of SARS-CoV-2 utilize human angiotensin-converting enzyme 2 (hACE2) as the receptor to infect human cells. Methods We combined molecular docking and surface plasmon resonance (SPR) to identify potential inhibitors for ACE2 from available commercial medicines. We also designed coronavirus pseudoparticles that contain the spike protein assembled onto green fluorescent protein or luciferase reporter gene-carrying vesicular stomatitis virus core particles. Results We found that thymoquinone, a phytochemical compound obtained from the plant Nigella sativa, is a potential drug candidate. SPR analysis confirmed the binding of thymoquinone to ACE2. We found that thymoquinone can inhibit SARS-CoV-2, SARS-CoV, and NL63 pseudoparticles infecting HEK293-ACE2 cells, with half-maximal inhibitory concentrations of 4.999, 7.598, and 6.019 μM, respectively. The SARS-CoV-2 pseudoparticle inhibition had half-maximal cytotoxic concentration of 35.100 μM and selection index = 7.020. Conclusion Thymoquinone is a potential broad-spectrum inhibitor for the treatment of coronavirus infections.

Background: Gastric cancer is highly aggressive disease. Despite advances in diagnosis and therapy, the prognosis is still poor. Various genetic and molecular alterations are found in gastric cancer that underlies the malignant transformation of gastric mucosa during the multistep process of gastric cancer pathogenesis. The detailed mechanism of the gastric cancer development remains uncertain. In present study we investigated the potential role of stathmin1 gene in gastric cancer tumorigenesis and examined the usefulness of RNA interference (RNAi) targeting stathmin1 as a form of gastric cancer treatment. Methods: A lentiviral vector encoding a short hairpin RNA (shRNA) targeted against stathmin1 was constructed and transfected into the packaging cells HEK 293 T and the viral supernatant was collected to transfect MKN-45 cells. The transwell chemotaxis assay and the CCK-8 assay were used to measure migration and proliferation of tumor cells, respectively. Quantitative real-time PCR and western blotting were used to detect the expression levels of stathmin1. Results: Lentivirus mediated RNAi effectively reduced stathmin1 expression in gastric cells. Significant decreases in stathmin1 mRNA and protein expression were detected in gastric cells carrying lentiviral stathmin-shRNA vector and also significantly inhibited the proliferation, migration in gastric cancer cells and tumorigenicity in Xenograft Animal Models. Conclusions: Our findings suggest that stathmin1 overexpression is common in gastric cancer and may play a role in its pathogenesis. Lentivirus mediated RNAi effectively reduced stathmin1 expression in gastric cells. In summary, shRNA targeting of stathmin1 can effectively inhibits human gastric cancer cell growth in vivo and may be a potential therapeutic strategy for gastric cancer.