Cheminformatics

化学信息学

• 批准号: 8336960
• 负责人: GERALD H LUSHINGTON
• 金额: $ 15.82万
• 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/8336960
• 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; 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.

如果一个人想象活动导向的合成类似于一个游戏的战舰，那么在硅片数据挖掘就像 声纳：而不是通过所有的化学空间爆炸附近的初步打击，直到有形的模式出现，一个 可以挖掘丰富的初步数据，以检测关键的潜在趋势，并相应地针对一个人的化学。 因此，在本文中，我们提出应用一系列计算协议来有效地传递化学洞察力， 将指导靶向合成具有实现探针状态的提高前景的命中类似物。我们的总体 目标是一个无缝的IT管道，用于获取、分析、存储和交付与以下内容相关的所有信息： 该专业化学中心（SCC）的科学功能，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联的元数据以适合于 分析和内部参考，并以格式报告所产生的分析、数据和元数据 所需的合成核心，原始筛选中心和MLPCN计划， 2.一系列专门的计算机筛选机制，允许（a） 初步筛选组内的生物活性簇，（B）鉴定大的现有生物活性簇的子集， 在物理化学上与化学空间的这些有希望的区域重叠的化合物集合， 以及（c）对新化学的直觉，这些新化学能够增强并潜在地改进现有的 生物活性物质， 3.高洞察力的定量构效关系（QSAR）模型，为有效的家庭， 阐明关键结构变体的生物活性物质具有满足可行探针标准的最佳前景， 和 4.对于化合物溶解度或其他实际问题的可靠的计算机预筛选， 来复合获取或合成。 我们获得了大量的计算和支持资源，致力于化学图书馆 开发，我们在应用上述方法进行探头开发方面的丰富经验， CMLD计划和PSL项目的一部分，我们的既定研究重点是开发新的 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。