VISUALIZING THOUSAND-DIMENSIONAL CHEMICAL DIVERSITY

可视化千维化学多样性

• 批准号: 6386403
• 负责人: DAVID E PATTERSON
• 金额: $ 38.05万
• 依托单位: CERTARA USA, INC.
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2003-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6386403
• 关键词: bioimaging /biomedical imaging; chemical information system; combinatorial chemistry; computer graphics /printing; computer human interaction; computer program /software; computer simulation; computer system design /evaluation; drug design /synthesis /production; drug discovery /isolation; high throughput technology; imaging /visualization /scanning; interactive multimedia; mathematical model

The goal of this work is to provide interactive computer visualizations which research scientists can use to interpret high throughput screening data and to make combinatorial chemistry choices. The simplest drug discovery principle is that compounds similar in enough properties are usually similar in biological activity. Similarity often involves measures in high-dimensional spaces, such as molecular fingerprints or shape descriptors. Uses of similarity in drug discovery research may apply to millions of compounds from virtual libraries of potentially synthesizable compounds. To examine relationships among vast numbers of compounds in diversity space, by simple graphical interactions with two dimensional maps of the space, allows the intuition of experienced scientists to come into play. The algorithms for visualization of thousand dimensional diversity spaces rely on horizons, which are distances beyond which the distance matrix need not be resolved, and on efficient subsampling methods. These concepts also enable selection of optimal descriptors to cluster compounds for predictive use, when combined in genetic algorithms. Optimal descriptors help not only in visualizing important features of diversity space, but in deciding which compounds to make and test next during early analoging of active substances. PROPOSED COMMERCIAL APPLICATION: Software that performs diversity selections needs these visualization tools. Compound libraries offered for random screening or following up on hits are more valuable when their designs can be illustrated. The tools apply to new areas such as differential gene expression data analysis. New methods for analyzing HTS data have commercial potential of improving the process of early drug discovery research.

这项工作的目标是提供交互式计算机可视化，研究科学家可以用来解释高通量筛选数据，并作出组合化学的选择。 最简单的药物发现原则是，在足够的性质上相似的化合物通常在生物活性上相似。 相似性通常涉及高维空间中的度量，例如分子指纹或形状描述符。 相似性在药物发现研究中的应用可能适用于来自潜在可合成化合物的虚拟库的数百万种化合物。 通过简单的图形交互与空间的二维地图来检查多样性空间中大量化合物之间的关系，允许有经验的科学家的直觉发挥作用。用于千维多样性空间的可视化的算法依赖于视野，视野是距离矩阵不需要被解析的距离，并且依赖于有效的子采样方法。 这些概念还使得能够选择最佳描述符，以在遗传算法中组合时将化合物聚类用于预测用途。 最佳描述符不仅有助于可视化多样性空间的重要特征，而且有助于在活性物质的早期模拟过程中决定下一步制备和测试哪些化合物。建议的商业应用：执行多样性选择的软件需要这些可视化工具。当化合物库的设计可以被说明时，为随机筛选或跟踪命中而提供的化合物库更有价值。 这些工具适用于新的领域，如差异基因表达数据分析。 HTS数据分析的新方法具有改善早期药物发现研究过程的商业潜力。