基于遗传算法寻找抗HBV活性分子的关键分子指纹片断

Key fingerprint fragments of anti-hepatitis B virus agents using genetic function approximation method

通过活性HBV DNA聚合酶抑制剂,采用二维定量构效关系方法寻找与活性关系相关的关键分子特征。模型采用遗传逼进算法,寻找关键的分子指纹片断,所得方程调整r2为0.911 9、预测r2为0.848 9。所获得的8个分子指纹片断药效特征与药效团模型相一致。这些分子指纹片断相较于片断库或随机片断更具有针对性,通过这些片断组装的分子库将极大地提高虚拟筛选和全新药物设计的效力。

In this paper quantitative structure-activity relationships （QSAR） were developed on HBV DNA poly- merase inhibitors to uncover the relationship between biological activity and the key structural features. The 2D QSAR model of fingerprint fragments was built using genetic function approximation （GFA） method with good internal （adjusted R-squared： 0. 911 9） and external prediction （rpred^2 = 0. 848 9）. The features of 8 fingerprint fragments obtained are consistent with 3D pharmacophore. These fingerprints are notably more effective than those based on a cule library fragment dictionary or hashing library. The combination of these fingerprint fragments to novel mole- provides an effective and efficient approach to virtual screening and de novo drug design.