Liu X H, Song H Y, Zhang J X, Han B C, Wei X N, Ma X H, Cui W K, Chen Y Z
Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Blk S16,Level 8, 3 Science Drive 2, Singapore 117543 phone: 65-6874-6877, fax: 65-6774-6756.
Institute of Materials Research and Engineering, A*STAR, 3 Research Link, Singapore 117602.
Mol Inform. 2010 May 17;29(5):407-20. doi: 10.1002/minf.200900014.
Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design.
组蛋白去乙酰化酶抑制剂（HDACi）已成功用于癌症和其他疾病的治疗。寻找新型的锌结合基团（ZBG）以及开发非异羟肟酸类HDACi已成为当前研究的重点。作为补充，需要探索一种虚拟筛选（VS）工具，该工具能够从大型化合物库中以类似于高通量筛选（HTS）的高产量和低假阳性率识别不同类型的潜在抑制剂。这项工作探索了使用支持向量机（SVM）结合我们新开发的假定非抑制剂生成方法作为这样一种工具。由2008年前的702种异羟肟酸类HDACi和64334种假定非HDACi训练的SVM在使用2008年以来报道的220种不同类型HDACi的交叉验证测试和独立测试中显示出良好的产量和低假阳性率。在扫描1356万个PubChem化合物和16.8万个MDDR化合物时，SVM的命中率与HTS率相当。对SVM虚拟命中物的进一步结构分析表明其在识别非异羟肟酸类HDACi方面的潜力。通过该分析，提出了一系列用于HDACi设计的新型ZBG和帽基。
