Cheminformatics

化学信息学

• 批准号: 7938923
• 负责人: GERALD H LUSHINGTON
• 金额: $ 17.86万
• 依托单位: UNIVERSITY OF KANSAS LAWRENCE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7938923
• 关键词: Algorithms; Assimilations; Biological; Blast Cell; Chemicals; Chemistry; Collection; Computer Simulation; Data; Data Analyses; Databases; Development; Environment; Family; Information Management; Intuition; Kansas; Metadata; Methods; Mining; Modeling; Pattern; Protocols documentation; Qualifying; Quantitative Structure-Activity Relationship; Reporting; Research; Resources; Screening procedure; Secure; Series; Services; Solubility; Specialized Center; Universities; Variant; analog; cheminformatics; data mining; experience; improved; insight; meetings; novel; programs; small molecule libraries; sonar; trend

If one imagines activity-directed synthesis to resemble a game of Battleship, then in silico data mining is like sonar: rather than blasting through all chemical space near preliminary hits until tangible patterns emerge, one can mine the wealth of preliminary data to detect key underlying trends and target one's chemistry accordingly. Herein we thus propose to apply a series of computational protocols to efficient delivery of chemical insight that will guide targeted synthesis of hit analogs with elevated prospects for achieving probe status. Our overarching objective is a seamless IT pipeline that acquires, analyzes, stores and delivers all information relevant to scientific function of this Specialized Chemistry Center (SCC), specifically focusing on delivering: 1. a robust, efficient and secure information management environment that enables assimilation of all data and metadata associated with a given screen into our own local databases in a format suitable for analysis and internal reference and reporting of resulting analyses, data and metadata in the formats required by the synthesis core, the originating screening center and the MLPCN program, 2. an array of specialized in silico screening mechanisms that permit (a) facile characterization of bioactive clusters within the preliminary screening set, (b) identification of subsets of large existing compound collections that physicochemically overlap with such promising regions of chemistry space, and (c) intuition of novel chemistries that stand to augment and potentially improve upon existing bioactives, 3. highly insightful quantitative structure-activity relationship (QSAR) models for potent families of bioactives that illuminate key structural variants with optimal prospects for meeting viable probe criteria, and 4. reliable in silico prescreens for compound solubility or other practical issues that should be gauged prior to compound acquisition or synthesis. Our access to a wealth of computational and support resources dedicated toward chemical library development, our extensive experience in the application of the above methods toward probe development as part of a CMLD program and PSL projects, and our established research focus on development of novel algorithms that enhance the biological relevance, target-sensitivity and chemical information content of modeling paradigms render our team particularly well qualified to deliver these services.

如果人们将活动导向的合成想象为一款战舰游戏，那么计算机数据挖掘就像 声纳：与其在初步命中附近爆破所有化学空间，直到出现有形的模式，不如 可以挖掘大量的初步数据来检测关键的潜在趋势并相应地瞄准一个人的化学反应。 因此，在这里，我们建议应用一系列计算协议来有效地传递化学见解， 将指导命中类似物的靶向合成，并提高实现探针状态的前景。我们的首要任务 目标是一个无缝的 IT 管道，用于获取、分析、存储和交付所有相关信息 该专业化学中心 (SCC) 的科学职能，特别侧重于提供： 1. 一个强大、高效和安全的信息管理环境，能够同化所有数据 以及与给定屏幕相关的元数据以适合的格式存储到我们自己的本地数据库中 分析和内部参考以及以格式报告结果分析、数据和元数据 合成核心、原始筛选中心和 MLPCN 程序所需的， 2. 一系列专门的计算机筛选机制，允许 (a) 轻松表征 初步筛选组内的生物活性簇，(b) 识别大型现有的子集 与化学空间中此类有前途的区域在物理化学上重叠的化合物集合， (c) 对新化学物质的直觉，这些化学物质可以增强并可能改进现有的化学物质 生物活性剂， 3. 具有高度洞察力的有效家族的定量构效关系（QSAR）模型 阐明关键结构变异的生物活性物质，具有满足可行探针标准的最佳前景， 和 4. 对化合物溶解度或其他应事先测量的实际问题进行可靠的计算机预筛选 化合物获取或合成。 我们可以获得专门用于化学库的大量计算和支持资源 开发，我们在将上述方法应用于探针开发方面拥有丰富的经验 CMLD 计划和 PSL 项目的一部分，我们既定的研究重点是开发新颖的 增强生物相关性、目标敏感性和化学信息内容的算法 建模范例使我们的团队特别有资格提供这些服务。