Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models

African swine fever virus (ASFV) causes a highly contagious and severe hemorrhagic viral disease with high mortality in domestic pigs of all ages. Although the virus is harmless to humans, the ongoing ASFV epidemic could have severe economic consequences for global food security. Recent studies have found a few antiviral agents that can inhibit ASFV infections. However, currently, there are no vaccines or antiviral drugs. Hence, there is an urgent need to identify new drugs to treat ASFV. Based on the structural information data on the targets of ASFV, we used molecular docking and machine learning models to identify novel antiviral agents. We confirmed that compounds with high affinity present in the region of interest belonged to subsets in the chemical space using principal component analysis and k-means clustering in molecular docking studies of FDA-approved drugs. These methods predicted pentagastrin as a potential antiviral drug against ASFVs. Finally, it was also observed that the compound had an inhibitory effect on AsfvPolX activity. Results from the present study suggest that molecular docking and machine learning models can play an important role in identifying potential antiviral drugs against ASFVs.     

Solubility and Stability of Some Pharmaceuticals in Natural Deep Eutectic Solvents-Based Formulations

Some medicines are poorly soluble in water. For tube feeding and parenteral administration, liquid formulations are required. The discovery of natural deep eutectic solvents (NADES) opened the way to potential applications for liquid drug formulations. NADES consists of a mixture of two or more simple natural products such as sugars, amino acids, organic acids, choline/betaine, and poly-alcohols in certain molar ratios. A series of NADES with a water content of 0–30% (w/w) was screened for the ability to solubilize (in a stable way) some poorly water-soluble pharmaceuticals at a concentration of 5 mg/mL. The results showed that NADES selectively dissolved the tested drugs. Some mixtures of choline-based NADES, acid-neutral or sugars-based NADES could dissolve chloral hydrate (dissociated in water), ranitidine·HCl (polymorphism), and methylphenidate (water insoluble), at a concentration of up to 250 mg/mL, the highest concentration tested. Whereas a mixture of lactic-acid–propyleneglycol could dissolve spironolacton and trimethoprim at a concentration up to 50 and 100 mg/mL, respectively. The results showed that NADES are promising solvents for formulation of poorly water-soluble medicines for the development of parenteral and tube feeding administration of non-water-soluble medicines. The chemical stability and bioavailability of these drug in NADES needs further studies.     

A Multi-Objective Approach for Drug Repurposing in Preeclampsia

Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as “targets”. The proteins labeled as “off-targets” were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)_α=160.9 = 0.258, the Enrichment Factor (EF)_1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia.     

Microwave-Assisted One-Pot Lipid Extraction and Glycolipid Production from Oleaginous Yeast Saitozyma podzolica in Sugar Alcohol-Based Media

Glycolipids are non-ionic surfactants occurring in numerous products of daily life. Due to their surface-activity, emulsifying properties, and foaming abilities, they can be applied in food, cosmetics, and pharmaceuticals. Enzymatic synthesis of glycolipids based on carbohydrates and free fatty acids or esters is often catalyzed using certain acyltransferases in reaction media of low water activity, e.g., organic solvents or notably Deep Eutectic Systems (DESs). Existing reports describing integrated processes for glycolipid production from renewables use many reaction steps, therefore this study aims at simplifying the procedure. By using microwave dielectric heating, DESs preparation was first accelerated considerably. A comparative study revealed a preparation time on average 16-fold faster than the conventional heating method in an incubator. Furthermore, lipids from robust oleaginous yeast biomass were successfully extracted up to 70% without using the pre-treatment method for cell disruption, limiting logically the energy input necessary for such process. Acidified DESs consisting of either xylitol or sorbitol and choline chloride mediated the one-pot process, allowing subsequent conversion of the lipids into mono-acylated palmitate, oleate, linoleate, and stearate sugar alcohol esters. Thus, we show strong evidence that addition of immobilized Candida antarctica Lipase B (Novozym 435^®), in acidified DES mixture, enables a simplified and fast glycolipid synthesis using directly oleaginous yeast biomass.     

医学留学生化学实验全英文教学资源建设与实践*

针对目前化学实验全英文在线教学资源匹配度较低、相对匮乏等问题,以医学留学生为中心,建设了有声课件、模块化实景实验操作视频以及测试题库3种不同类型的化学实验全英文教学资源,通过资源的线下使用和基于雨课堂的在线预习,实现从“集中、定时、定点”的传统教学模式向“模块化、碎片化、信息化”的混合教学模式转变,可以丰富教学资源和教学手段,改进教学秩序,提高医学留学生化学知识和技能学习的积极性和主动性,使“教”“学”良性循环,可切实提高化学实验国际化教学质量。     

促进深度学习的专题复习教学*——以“碳及其化合物”为例

以促进学生的深度学习为基本理念,围绕“自然界的碳循环”“大气中CO₂的控减排”“化学家们合成的新型碳家族成员”等板块,设计并成功实施系列情境问题,在系列问题的讨论、分析、解决过程中帮助学生建构“碳”家族成员间相互转化的知识网络,提高学生思维的纵深度,引发高阶思维,促进深度学习。     

“学习提示”模块在新版有机化学教材中的应用

一本好的有机化学教材应该对教师教学和学生自学有很好的辅助和引导作用。如何使学生从浩如烟海的内容中发现重点、难点,并做到有效掌握和融会贯通,是教材编写者需要重点考虑的问题。笔者将教学经验融入到有机化学教材改革实践,在新版教材中加入了原创的“学习提示”板块,用简练、更通俗易懂的语言对一些重要、较难理解的或是容易混淆的知识点进行辅助讲解、串联总结。笔者认为该板块可以起到协助学生理解知识难点、厘清易混淆概念、将关联知识融会贯通以及激发学生科研兴趣等作用。本文用一些实例对这一模块的编写构思、内容和预期成效进行了介绍。     

基于机器学习的直接电离质谱爆炸物检测方法

直接电离质谱系统在现场快速检测中的应用日益广泛,主要用于爆炸物、毒品、食品添加剂等的检测。然而,直接电离质谱系统中质谱信号波动大且同一浓度样品峰强呈现对数正态分布,严重影响了检出限附近低浓度样品的检测准确性。该研究将乙酰水杨酸(115个样品)作为爆炸物模拟物,利用介质阻挡放电离子源与质谱系统,研究了基于机器学习的直接电离质谱数据预处理和分类算法,以提高低浓度样品的检测准确率。对两种浓度为1 ng/mL的常见爆炸物样本(三硝基甲苯和硝酸铵分别为110、90个)及空白对照样本(366个)开展了应用实验。结果表明,与传统提取离子流方法和高斯混合模型方法相比,采用随机森林算法可将F_score从0.74、0.89提升至0.96,显著提高了检测准确率,且单个样本数据分析时间远少于0.1 s,满足实时检测需求。