Reproducible, Unbiased Ligand Identification Assisted by Artificial Intelligence and Development of Ligand Reference Libraries

我们目前对疾病分子机制的理解和基于结构的设计 用于治疗的药物，依赖于实验确定的蛋白质和其他 大分子与小分子配体形成络合。许多这样的结构都有直接的 与公众健康有关，特别是药物靶标与药物、抑制剂、底物、 或变构效应器。然而，基于结构的药物发现是极其复杂和困难的。 由实验偏倚和现行实验配基方法的不足 识别，这往往导致识别错误、丢失或放错了配基。传播 错误的结构与增加的结构数据的可访问性相结合，不仅 阻碍了生物医学研究和药物发现的重复性，但也转移了有价值的 资源枯竭注定是一条研究之路。我们将利用我们丰富的经验来验证 以及提纯配基结合位点以生成将公开制作的配基参考库 可在一个新的网络资源上获得，该资源致力于小分子和 大分子。这些文库可用于许多下游应用，如药物 设计、计算化学、生物学和生物信息学。我们将利用最近的 机器学习中的技术进步与现有工具相结合，以创建 密度解释的标准化协议和无偏见、可重复的配基 身份证明。这条管道将不仅能够识别和模拟未分配密度的配体 片段，但也能够检测和纠正现有的次优精炼配体 结构。由于拟议的人工智能将不存在认知偏见，它应该会缓解最严重的 基于结构的药物设计中存在的问题。因为未正确解释的结构可能具有 显著的有害涟漪效应，我们将通过实验验证选定的生物医学重要 利用X射线对关键小分子具有可疑实验支持的结构 结晶学或电子显微镜。

Our current understanding of the molecular mechanisms of disease and structure-based design of drugs for treatment, rely on experimentally determined 3D structures of proteins and other macromolecules complexed with small molecule ligands. Many of these structures have direct relevance to public health, especially complexes of drug targets with drugs, inhibitors, substrates, or allosteric effectors. Yet, structure-based drug discovery is severely complicated and hindered by experimental bias and the shortcomings of current methods of experimental ligand identification, which often result in misidentified, missing, or misplaced ligands. The propagation of erroneous structures combined with an increased accessibility to structural data not only thwarts reproducibility in biomedical research and drug discovery, but also diverts valuable resources down doomed research avenues. We will leverage our extensive experience validating and refining ligand binding sites to generate ligand reference libraries that will be made publically available on a new web resource dedicated to the interaction of small molecules and macromolecules. These libraries can be used in many downstream applications, such as drug design, computational chemistry, biology, and bioinformatics. We will utilize recent technological advances in machine learning in conjunction with existing tools to create a standardized protocol for density interpretation and unbiased, reproducible ligand identification. This pipeline will not only be able identify and model ligands in unassigned density fragments, but also be able to detect and correct suboptimally refined ligands in existing structures. As the proposed AI will be free from cognitive bias, it should alleviate the most severe problems in structure-based drug design. Because improperly interpreted structures can have a significant deleterious ripple effect, we will experimentally verify select biomedically important structures with dubious experimental support for critical small molecules using use X-ray crystallography or electron microscopy.