Data Science and Engineering, Lilly Research Laboratories, Eli Lilly and Company, Erl Wood Manor, Windlesham, Surrey GU20 6PH, United Kingdom.
Computational ADME, ADME-Toxicology-PKPD, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States.
J Chem Inf Model. 2020 Oct 26;60(10):4757-4771. doi: 10.1021/acs.jcim.0c00583. Epub 2020 Oct 6.
Matched Molecular Pairs (MMP) analysis is a well-established technique for Structure Activity and Property Analysis (SAR and SPR). Summarizing multiple MMPs that describe the same structural change into a single chemical transform can be a powerful tool for prediction (termed Transform from here on). This is particularly useful in the area of Absorption, Distribution, Metabolism, and Elimination (ADME) analysis that is less influenced by 3D structural binding effects. The creation of a knowledge database containing many of these Transforms across typical ADME assays promises to be a powerful approach to aid multidimensional optimization. We present a detailed workflow for the derivation of such a database. We include details of an MMP fragmentation algorithm with associated statistical summarization methods for the derivation of Transforms. This is made freely available as part of the LillyMol software package. We describe the application of this method to several ADME/Tox (Toxicity) assay data sets and highlight multiple cases where the impact of traditional medicinal chemistry Transforms is contradicted by MMP data. We also describe the internal software interface used by medicinal chemists to aid the design of new compounds via automated suggestion. This approach utilizes the matched pairs database to "suggest" improved compounds in an automated design scenario. A nonvisual script-based version of the automated suggestions code with an associated set of described chemical Transforms is also made freely available along with this paper and as part of the LillyMol software package. Finally, we contrast this knowledge database against a larger database of all MMPs derived from a 2 million compound diversity set and a subset of MMPs seen in historical discovery projects. The comparison against all transforms in the diversity collection highlights the very low coverage of the transform database as compared to all possible transforms involving 15 atom fragments. The comparison against a smaller subset of Transforms seen on internal Medicinal Chemistry projects shows better coverage of the transform database for a small set of common medicinal chemistry strategies. Within the context of all possible transforms available to a medicinal chemistry project team, the challenge remains to move beyond mere idea generation from past projects toward high quality prediction for novel ADME/Tox modulating Transforms.
匹配分子对（MMP）分析是一种成熟的结构活性和性质分析（SAR 和 SPR）技术。将描述相同结构变化的多个 MMP 总结为单个化学转化，可以成为预测的有力工具（从这里称为 Transform）。这在吸收、分布、代谢和消除（ADME）分析领域特别有用，因为它受 3D 结构结合效应的影响较小。创建一个包含许多典型 ADME 测定中这些 Transform 的知识数据库，有望成为辅助多维优化的有力方法。我们提出了一个详细的工作流程，用于推导这样的数据库。我们包括一个 MMP 片段算法的详细信息，以及用于推导 Transform 的相关统计总结方法。这作为 LillyMol 软件包的一部分免费提供。我们描述了该方法在几个 ADME/Tox（毒性）测定数据集上的应用，并突出了多个 MMP 数据与传统药物化学 Transform 相矛盾的情况。我们还描述了药物化学家用于通过自动化建议来辅助新化合物设计的内部软件接口。这种方法利用匹配对数据库在自动化设计场景中“建议”改进的化合物。本文还与 LillyMol 软件包一起提供了一个带有描述性化学 Transform 的非可视化脚本化版本的自动化建议代码。最后，我们将这个知识数据库与从 200 万化合物多样性集中衍生的所有 MMP 的更大数据库以及历史发现项目中看到的 MMP 子集进行对比。与多样性集中的所有 Transform 进行对比突出了 Transform 数据库的覆盖率非常低，与涉及 15 个原子片段的所有可能 Transform 相比。与内部药物化学项目中看到的较小 Transform 子集进行对比显示，对于一组较小的常见药物化学策略，Transform 数据库的覆盖率更好。在药物化学项目团队可获得的所有可能 Transform 的背景下，挑战仍然是从过去的项目中不仅仅是生成想法，而是朝着新颖的 ADME/Tox 调节 Transform 的高质量预测前进。
