Pulled Flowprobe for Ambient Liquid Extraction-Based High Spatial Resolution Mass Spectrometry Imaging with Enhanced Sensitivity and Stability.

Ambient liquid extraction techniques enable direct mass spectrometry imaging (MSI) under ambient conditions with minimal sample preparation. However, currently an integrated probe for ambient liquid extraction-based MSI with high spatial resolution, high sensitivity, and stability is still lacking. In this work, we developed a new integrated probe made of pulled coaxial capillaries, named pulled flowprobe, and compared it with the previously reported single-probe. Mass transfer kinetics in probes was first investigated. The extraction kinetic curves during probe sampling indicate a narrower and higher peak shape for the pulled flowprobe than single-probe. Computational fluid dynamics analysis reveals that in the pulled flowprobe flow velocities are lower in liquid microjunction and higher in the transferring channels, resulting in higher extraction efficiencies and reduced band diffusion compared with single-probe and other probes with a similar flow route. Results of ambient liquid extraction-based MSI of lipids in rat cerebrum show that signals of low-abundance lipids were 2-5 times higher via a pulled flowprobe than via a single-probe, and 26 more lipid species were detected on brain tissue via a pulled flowprobe than via a single-probe. The stability of MSI with the pulled flowprobe was found to be higher than that with single-probe (averaged relative standard deviation = 18% vs 80%) by imaging a lab-made uniform ink coating. Moreover, in the pulled flowprobe, no retraction of the inner capillary from outer capillary is optimal for both sensitivity and stability. The spatial resolution of the pulled flowprobe (30-40 µm) was measured to be higher than that of a comparable size single-probe by calculation with the "80-20" rule. Finally, the new pulled flowprobe was applied to high-resolution MSI of lipids in the hippocampus, and localization of several lipids to the specific cell layers in the hippocampus region was observed. Thus, this work provides an alternative easily fabricated sampling probe with enhanced sensitivity, stability, and spatial resolution, promoting the use of ambient liquid extraction-based MSI in biological and clinical research.

Prediction of drug-likeness using graph convolutional attention network.

MOTIVATION: The drug-likeness has been widely used as a criterion to distinguish drug-like molecules from non-drugs. Developing reliable computational methods to predict the drug-likeness of compounds is crucial to triage unpromising molecules and accelerate the drug discovery process. RESULTS: In this study, a deep learning method was developed to predict the drug-likeness based on the graph convolutional attention network (D-GCAN) directly from molecular structures. Results showed that the D-GCAN model outperformed other state-of-the-art models for drug-likeness prediction. The combination of graph convolution and attention mechanism made an important contribution to the performance of the model. Specifically, the application of the attention mechanism improved accuracy by 4.0%. The utilization of graph convolution improved the accuracy by 6.1%. Results on the dataset beyond Lipinski's rule of five space and the non-US dataset showed that the model had good versatility. Then, the billion-scale GDB-13 database was used as a case study to screen SARS-CoV-2 3C-like protease inhibitors. Sixty-five drug candidates were screened out, most substructures of which are similar to these of existing oral drugs. Candidates screened from S-GDB13 have higher similarity to existing drugs and better molecular docking performance than those from the rest of GDB-13. The screening speed on S-GDB13 is significantly faster than screening directly on GDB-13. In general, D-GCAN is a promising tool to predict the drug-likeness for selecting potential candidates and accelerating drug discovery by excluding unpromising candidates and avoiding unnecessary biological and clinical testing. AVAILABILITY AND IMPLEMENTATION: The source code, model and tutorials are available at https://github.com/JinYSun/D-GCAN. The S-GDB13 database is available at https://doi.org/10.5281/zenodo.7054367. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.