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

我们目前对疾病的分子机制和基于结构的设计的理解 治疗药物，依赖于实验确定的蛋白质和其他 与小分子配体复合的大分子。这些结构中的许多具有直接的 与公共卫生的相关性，特别是药物靶标与药物、抑制剂、底物的复合物， 或变构效应物。然而，基于结构的药物发现是严重复杂和阻碍的 受实验偏差和目前实验配体方法的不足 这通常导致配体的错误识别、缺失或错位。传播 错误的结构与结构数据的可访问性的增加相结合，不仅 阻碍了生物医学研究和药物发现的可重复性，但也转移了有价值的 资源投入到注定失败的研究领域我们将利用我们丰富的经验， 以及精制配体结合位点以产生配体参考文库， 可在一个新的网络资源，致力于小分子的相互作用， 大分子这些文库可用于许多下游应用，例如药物代谢、生物学和生物医学。 设计、计算化学、生物学和生物信息学。我们将利用最近 机器学习的技术进步与现有工具相结合， 用于密度解释和无偏、可再现配体的标准化方案 识别.该管道不仅能够识别和建模未分配密度的配体， 片段，而且还能够检测和校正现有的未优化的精制配体。 结构.由于拟议中的人工智能将不受认知偏见的影响，它应该会减轻最严重的 基于结构的药物设计中的问题。因为不正确解释的结构可能会导致 显著有害的涟漪效应，我们将通过实验验证选择生物医学重要 用X射线对临界小分子进行可疑实验支持的结构 晶体学或电子显微镜。

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.