Fukunishi Yoshifumi, Kubota Satoru, Nakamura Haruki
Biological Information Research Center (BIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6, Aomi, Koto-ku, Tokyo 135-0064, Japan.
J Chem Inf Model. 2006 Sep-Oct;46(5):2071-84. doi: 10.1021/ci060152z.
We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.
我们开发了一种使用分子相互作用矩阵来提高分子相互作用数据准确性的新方法。该方法应用于通过蛋白质-化合物对接软件计算的蛋白质-化合物亲和力矩阵,增强虚拟药物筛选和虚拟靶蛋白筛选的数据库富集。我们的假设是,化合物的蛋白质-化合物结合自由能可以通过其与许多不同蛋白质的对接分数的线性组合来提高。我们提出了两种确定线性组合系数的方法。第一种方法基于蛋白质之间的相似性,第二种是基于已知活性化合物的机器学习方法。这些方法应用于几种靶蛋白活性化合物的虚拟筛选和虚拟靶蛋白筛选。