Microfluidic platform for combinatorial synthesis in picolitre droplets

This paper presents a droplet-based microfluidic platform for miniaturized combinatorial synthesis. As a proof of concept, a library of small molecules for early stage drug screening was produced. We present an efficient strategy for producing a 7 × 3 library of potential thrombin inhibitors that can be utilized for other combinatorial synthesis applications. Picolitre droplets containing the first type of reagent (reagents A(1), A(2), …, A(m)) were formed individually in identical microfluidic chips and then stored off chip with the aid of stabilizing surfactants. These droplets were then mixed to form a library of droplets containing reagents A(1-m), each individually compartmentalized, which was reinjected into a second microfluidic chip and combinatorially fused with picolitre droplets containing the second reagent (reagents B(1), B(2), …, B(n)) that were formed on chip. The concept was demonstrated with a three-component Ugi-type reaction involving an amine (reagents A(1-3)), an aldehyde (reagents B(1-7)), and an isocyanide (held constant), to synthesize a library of small molecules with potential thrombin inhibitory activity. Our technique produced 10(6) droplets of each reaction at a rate of 2.3 kHz. Each droplet had a reaction volume of 3.1 pL, at least six orders of magnitude lower than conventional techniques. The droplets can then be divided into aliquots for different downstream screening applications. In addition to medicinal chemistry applications, this combinatorial droplet-based approach holds great potential for other applications that involve sampling large areas of chemical parameter space with minimal reagent consumption; such an approach could be beneficial when optimizing reaction conditions or performing combinatorial reactions aimed at producing novel materials.     

磺酰脲类化合物除草活性的QSAR研究

采用密度泛函理论方法, 在B3LYP/6-31G(d)水平下, 计算了23种磺酰类化合物的分子极化率及分子骨架中各原子的Milliken电荷. 提出了一种新的QSAR建模方法, 并据此对其中18种化合物进行多元线性回归分析, 建立了除草活性的预测模型(R=0.96, R2=0.92, r2adj=0.88, F=26.26, q2=0.71, p<0.01, SE=0.36), 对剩余五种化合物进行预测, 结果吻合. 该模型从化合物的亲水性、分子几何特征的角度对如何提高磺酰脲类化合物的除草活性进行了分析, 并对提高化合物除草活性的方法做出预测: 提高苯环和嘧啶环取代基的亲水性, 增加N13周围的电子云密度, 为苯环接入较小的取代基团, 在嘧啶环上接入较大取代基团都可提高化合物的除草活性. 预测结果与3D-QSAR方法的预测结果一致.