Cheminformatics

如果有人将活动导向的合成想象成类似于战舰游戏，那么在计算机数据挖掘中就像 声纳：不是在初步命中附近的所有化学空间爆炸，直到出现切实的模式，而是一种 可以挖掘大量的初步数据，以发现关键的潜在趋势，并相应地针对自己的化学成分。 在此，我们建议应用一系列计算协议来有效地提供化学洞察力 将指导命中类似物的定向合成，提高实现探测状态的可能性。我们最重要的是 目标是一条无缝的IT渠道，可获取、分析、存储和交付与以下各项相关的所有信息 这个专门的化学中心(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，能够吸收所有数据 和与给定屏幕相关联的元数据放入我们自己的本地数据库中 以下列格式分析、内部参考和报告结果分析、数据和元数据 合成核心、始发筛选中心和MLPCN计划所要求的， 2.一系列专门的硅胶筛选机制，使(A)能够方便地确定 初步筛选集内的生物活性集群，(B)识别大型现有 与化学空间的这些有希望的区域在物理化学上重叠的化合物集合， 以及(C)对新化学的直觉，这些化学将在现有化学的基础上得到加强和潜在的改进 生物活性物质， 3.有效家族的高洞察力定量构效关系(Qsar)模型 生物活性，阐明了关键的结构变体，具有满足可行探测标准的最佳前景， 和 4.在硅胶预筛中可靠地处理化合物的溶解度或应事先测量的其他实际问题 合成合成获得或合成化合物 我们访问了大量的计算和支持资源，专门用于化学库 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，我们已建立的研究重点是小说的开发 增强生物相关性、目标敏感度和化学信息含量的算法 建模范例使我们的团队特别有资格交付这些服务。

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.