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: 10241834
• 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/10241834
• 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 Collaborative Drug Discovery，Inc. (CDD)建议开发一种新的智能数据浏览器， 药物化学家开发新的药物化合物，以更有效地浏览和组织实验 数据以直观的方式。拟议中的浏览器将基本上“超链接”化学空间，并允许化学家 在化学铅系列中的化合物之间轻松导航，遵循从一个化合物开始的相同途径， 在他们头脑中直观映射的心智模型中与下一个复合。导航和对其 扩展先导系列，以发现最佳候选药物，推进动物研究和临床试验 包括药物发现管道的关键阶段：后续大规模投资的成功取决于 做出正确的决定这个阶段也特别强调创造性和直觉思维。现有 一种帮助科学家从事这项工作的软件，将数据以难以组合的格式制成表格 并比较所需的基本数据，以快速探索如何进一步优化有前途的 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。 这项技术的一个关键创新是我们开发的一种方法， 分子结构通过基于作为Hasse的子结构-超结构关系的部分排序 图表。我们的半格表示提供了一个机器可计算的格式，可以捕获 药物化学家凭直觉所知的相关化学实体之间的关系。 预期的主要影响包括：（1）加快将先导系列药物开发为候选药物，（2）由于 更有效地利用合成和分析资源，最重要的是（3）更好的科学决策 关于哪些化合物应该被应用到临床上。更好的决策在这个阶段， 药物发现过程应该增加被选择的候选药物成功的概率， 作为FDA批准的药物通过临床管道出现，并提高药物的有效性和安全性 那些毒品即使这些概率的微小增加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。我们已经验证了这种价值观， 对潜在的制药公司客户进行初步的市场调查。 !