普通有机分子或药物热分解稳定性的理论预测

采用密度泛函理论方法, 在B3LYP/6-31+G(d,p)水平上, 对任意选定的32个有机化合物或药物进行最低能量构象优化和结构参数理论计算. 建立了四极矩参数Q_ii与半数摩尔热分解函数Y_d(1/2)的相关方程, 其定量构性关系(QSPR)方程为Y_d(1/2)=-8.65747-3.8954Q_ii, 相关系数为r²=-0.99297, 交叉验证相关系数为XV-r²=0.99188, F检验结果为4237.343321. 训练集化合物的半数分解温度T_d(1/2)的平均绝对预测误差(AVEDEV)为14.70 K. 进一步利用该方程对测试集中43个分子进行预测验证, T_d(1/2)的预测值与实验值的相关系数为0.92304, Y_d的预测值与实验值的相关系数为0.99345, 证实了所建立方法的可靠性. 结构差异性分析表明, 训练集和测试集中的化合物均较均匀地分布在结构参数的3D空间中, 化合物结构具有较好的多样性和差异性.     

Design of a compound screening collection for use in high throughput screening

In this paper we introduce a quantitative model that relates chemical structural similarity to biological activity, and in particular to the activity of lead series of compounds in high-throughput assays. From this model we derive the optimal screening collection make up for a given fixed size of screening collection, and identify the conditions under which a diverse collection of compounds or a collection focusing on particular regions of chemical space are appropriate strategies. We derive from the model a diversity function that may be used to assess compounds for acquisition or libraries for combinatorial synthesis by their ability to complement an existing screening collection. The diversity function is linked directly through the model to the goal of more frequent discovery of lead series from high-throughput screening. We show how the model may also be used to derive relationships between collection size and probabilities of lead discovery in high-throughput screening, and to guide the judicious application of structural filters.     

Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures

Incorporating receptor flexibility is considered crucial for improvement of docking-based virtual screening. With an abundance of crystallographic structures freely available, docking with multiple crystal structures is believed to be a practical approach to cope with protein flexibility. Here we describe a successful application of the docking of multiple structures to discover novel and potent Chk1 inhibitors. Forty-six Chk1 structures were first compared in single structure docking by predicting the binding mode and recovering known ligands. Combinations of different protein structures were then compared by recovery of known ligands and an optimal ensemble of Chk1 structures were selected. The chosen structures were used in the virtual screening of over 60?000 diverse compounds for Chk1 inhibitors. Six novel compounds ranked at the top of the hits list were tested experimentally, and two of these compounds inhibited Chk1 activity-the best with an IC(50) value of 9.6 μM. Further study indicated that achieving a better enrichment and identifying more diverse compounds was more likely using multiple structures than using only a single structure even when protein structures were randomly selected. Taking into account conformational energy difference did not help to improve enrichment in the top ranked list.