How to generate reliable and predictive CoMFA models.
How to generate reliable and predictive CoMFA models.
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DOI：
10.2174/092986711794927702
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发表时间：
2011-01
影响因子：
4.1
通讯作者：
Lei Zhang;K. Tsai;Lupei Du;H. Fang;Minyong Li;Wenfang Xu
中科院分区：
文献类型：
--
作者：
Lei Zhang;K. Tsai;Lupei Du;H. Fang;Minyong Li;Wenfang Xu
比较分子场分析（Comparative Molecular Field Analysis，CoMFA）是药物发现和开发领域的一种主流和实用的三维定量构效关系（3D QSAR）技术。尽管CoMFA具有很高的预测能力，但其固有的数据依赖性仍然使其受到噪声的影响。众所周知，CoMFA中的默认设置可以产生预测的QSAR模型，同时优化的参数被证明可以提供更好的预测结果。因此，到目前为止，已经完成了许多努力，以改善CoMFA模型的鲁棒性和预测精度，通过考虑各种因素，包括分子构象和对齐，字段描述符和网格间距。在这里，我们想做一个全面的调查，可以想象的描述符和他们的贡献的CoMFA模型的预测能力。
Comparative Molecular Field Analysis (CoMFA) is a mainstream and down-to-earth 3D QSAR technique in the coverage of drug discovery and development. Even though CoMFA is remarkable for high predictive capacity, the intrinsic data-dependent characteristic still makes this methodology certainly be handicapped by noise. It's well known that the default settings in CoMFA can bring about predictive QSAR models, in the meanwhile optimized parameters was proven to provide more predictive results. Accordingly, so far numerous endeavors have been accomplished to ameliorate the CoMFA model's robustness and predictive accuracy by considering various factors, including molecular conformation and alignment, field descriptors and grid spacing. Herein, we would like to make a comprehensive survey of the conceivable descriptors and their contribution to the CoMFA model's predictive ability.