School of Physical Sciences, The University of Queensland, St. Lucia, Queensland 4072, Australia.
J Comput Chem. 2011 Jun;32(8):1541-9. doi: 10.1002/jcc.21732. Epub 2011 Feb 1.
A new population-based incremental learning algorithm for conformational searching of molecules is presented. This algorithm is particularly effective at determining, by relatively small number of energy minimizations, global energy minima of large flexible molecules. The algorithm is also able to find a large set of low energy conformations of more rigid small molecules. The performance of the algorithm is relation to other algorithm is examined via the test molecules: C(18) H(38) , C(39)H(80) , cycloheptadecane and a set of five drug-like molecules.
本文提出了一种新的基于群体的增量学习算法，用于分子构象搜索。该算法在通过相对较少的能量最小化来确定大柔性分子的全局能量最小值方面非常有效。该算法还能够找到更多刚性小分子的大量低能构象。通过测试分子：C(18)H(38)、C(39)H(80)、环十七烷和一组五个类似药物的分子，检查了该算法与其他算法的性能关系。
