Baskin Igor I, Solov'ev Vitaly P, Bagatur'yants Alexander A, Varnek Alexandre
Faculty of Physics, M.V. Lomonosov Moscow State University, Leninskie Gory, Moscow, Russian Federation, 119991.
Laboratory of Chemoinformatics, Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russian Federation.
J Comput Aided Mol Des. 2017 Aug;31(8):701-714. doi: 10.1007/s10822-017-0033-6. Epub 2017 Jul 7.
Generative topographic mapping (GTM) approach is used to visualize the chemical space of organic molecules (L) with respect to binding a wide range of 41 different metal cations (M) and also to build predictive models for stability constants (logK) of 1:1 (M:L) complexes using "density maps," "activity landscapes," and "selectivity landscapes" techniques. A two-dimensional map describing the entire set of 2962 metal binders reveals the selectivity and promiscuity zones with respect to individual metals or groups of metals with similar chemical properties (lanthanides, transition metals, etc). The GTM-based global (for entire set) and local (for selected subsets) models demonstrate a good predictive performance in the cross-validation procedure. It is also shown that the data likelihood could be used as a definition of the applicability domain of GTM-based models. Thus, the GTM approach represents an efficient tool for the predictive cartography of metal binders, which can both visualize their chemical space and predict the affinity profile of metals for new ligands.
生成地形映射(GTM)方法用于可视化有机分子(L)与41种不同金属阳离子(M)结合的化学空间,还使用“密度图”“活性景观”和“选择性景观”技术构建1:1(M:L)配合物稳定常数(logK)的预测模型。描述2962种金属结合剂全集的二维图揭示了相对于具有相似化学性质的单个金属或金属组(镧系元素、过渡金属等)的选择性和混杂区域。基于GTM的全局(针对整个数据集)和局部(针对选定子集)模型在交叉验证过程中表现出良好的预测性能。研究还表明,数据似然性可作为基于GTM模型适用域的定义。因此,GTM方法是金属结合剂预测制图的有效工具,它既能可视化其化学空间,又能预测金属对新配体的亲和性概况。