Shemetulskis N E, Weininger D, Blankley C J, Yang J J, Humblet C
Parke-Davis Pharmaceutical Research Division, Warner-Lambert Company, Ann Arbor, Michigan 48105, USA.
J Chem Inf Comput Sci. 1996 Jul-Aug;36(4):862-71. doi: 10.1021/ci950169+.
An algorithm, Stigmata, is described, which extracts structural commonalities from chemical datasets. It is discussed using several illustrative examples and a pharmaceutically interesting set of dopamine D2 agonists. The commonalities are determined using two-dimensional topological chemical descriptions and are incorporated into the key feature of the algorithm, the modal fingerprint. Flexibility is built into the algorithm by means of a user-defined threshold value, which affects the information content of the modal fingerprint. The use of the modal fingerprint as a diversity assessment tool, as a database similarity query, and as a basis for color mapping the determined commonalities back onto the chemical structures is demonstrated.
本文描述了一种名为“Stigmata”的算法，它可从化学数据集中提取结构共性。文中通过几个示例以及一组具有药学意义的多巴胺D2激动剂对该算法进行了讨论。这些共性是通过二维拓扑化学描述来确定的，并被纳入算法的关键特征——模态指纹中。该算法通过用户定义的阈值来构建灵活性，该阈值会影响模态指纹的信息含量。文中展示了将模态指纹用作多样性评估工具、数据库相似性查询以及将所确定的共性映射回化学结构进行颜色映射的基础。
