Intelligent Chemical Structure Browser for Drug Discovery and Optimization

用于药物发现和优化的智能化学结构浏览器

• 批准号: 10386918
• 负责人: BARRY A BUNIN
• 金额: $ 72.73万
• 依托单位: COLLABORATIVE DRUG DISCOVERY, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10386918
• 关键词: Algorithms; Animals; Biological Assay; Chemical Structure; Chemicals; Chemistry; Clinical; Clinical Trials; Complex; Computer software; Cost Savings; Data; Databases; Development; Disease; Drug Compounding; Effectiveness; FDA approved; Graph; Head; Ingestion; Intelligence; Intuition; Investments; Lead; Libraries; Maps; Market Research; Methodology; Methods; Modeling; Molecular Structure; Mutate; Neighborhoods; Pathway Analysis; Pathway interactions; Perception; Performance; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Phase; Probability; Process; Property; Psyche structure; Resources; Safety; Scientist; Series; Site; Structure; Structure-Activity Relationship; Suggestion; Technology; Testing; Textbooks; Thinking; Time; Translating; Vision; automated analysis; base; chemical group; drug candidate; drug discovery; experience; grasp; improved; innovation; lead candidate; lead series; novel; novel therapeutics; operation; parallelization; performance tests; scaffold; success

PROJECT SUMMARY Collaborative Drug Discovery, Inc. (CDD) proposes to develop a novel intelligent data browser that will enable medicinal chemists developing new drug compounds to more efficiently browse and organize experimental data in an intuitive way. The proposed browser will essentially “hyperlink” chemical space and allow chemists to navigate easily among compounds in a chemical lead series following the same pathways that lead from one compound to the next in the mental models that they intuitively map in their heads. Navigating through and extending a lead series to discover the optimal drug candidate to advance into animal studies and clinical trials comprises a critical stage of the drug discovery pipeline: the success of large subsequent investments depends on making the right decision. This stage also especially emphasizes creative and intuitive thinking. Existing software that assists scientists engaged in this task tabulates data in formats that make it difficult to assemble and compare the essential data needed to rapidly explore ideas about how to further optimize promising candidates. Our proposed intelligent browser will support more natural and intuitive workflows. A key enabling innovation for this technology is a methodology that we have developed to organize molecular structures through a partial ordering based on the substructure-superstructure relation as a Hasse diagram. Our semilattice representation provides a machine computable format that can capture the relationships among related chemical entities that a medicinal chemist intuits. Expected key impacts include (1) faster development of lead series into drug candidates, (2) cost savings due to more efficient use of synthesis and assay resources, and most importantly (3) better scientific decisions about which compounds to pursue and advance into the clinical pipeline. Better decisions at this stage in the drug discovery process should increase the probability that drug candidates that are chosen will successfully emerge through the clinical pipeline as FDA approved drugs, and improve the effectiveness and safety profile of those drugs. Even a small increase in these probabilities multiplied by the size of the investments required to take drugs through clinical trials translates into a large value. We have validated this perception of value in preliminary market research with potential pharmaceutical company customers. !

项目摘要 协作药物发现公司（CDD）提案，以开发一个新颖的智能数据浏览器，该浏览器将启用 医学化学家开发新药化合物以更有效地浏览并组织实验 以直观的方式进行数据。提出的浏览器本质上将“超链接”化学空间，并允许化学家 在化学铅系列中轻松在化合物中轻松导航，遵循相同的途径。 在他们直观地映射到脑海中的心理模型中的下一个复合物。导航和 扩展铅系列以发现最佳的候选药物，以进步进入动物研究和临床试验 包括药物发现管道的关键阶段：随后大量投资的成功取决于 做出正确的决定。这个阶段还特别强调了创造性和直观的思维。现存的 协助从事此任务的科学家的软件将数据制成数据，以使其难以组装 并比较快速探索有关如何进一步优化承诺所需的基本数据 候选人。我们提出的智能浏览器将支持更自然和直观的工作流程。 为这项技术创新的关键是我们开发的一种方法论 基于子结构 - 实体关系的部分顺序通过部分订购作为HASSE的分子结构 图表。我们的半静音表示提供了可计算的机器，可以捕获 药物化学家直觉的相关化学实体之间的关系。 预期的关键影响包括（1）更快地开发铅系列候选人，（2）节省成本 更有效地利用合成和测定资源，最重要的是（3）更好的科学决策 关于哪些化合物可以追求并晋升为临床管道。在这个阶段的更好决定 药物发现过程应增加选择的候选药物的可能性 作为FDA批准的药物，通过临床管道出现，并提高了 那些药物。即使这些可能性的略有增加却乘以所需的投资规模 通过临床试验服用药物，可以转化为巨大的价值。我们已经验证了对价值的看法 与潜在的制药公司客户的初步市场研究。 呢