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

人工智能辅助的可重复、公正的配体鉴定和配体参考文库的开发

基本信息

批准号：
10019572
负责人：
WLADEK MINOR
金额：
$ 56.12万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-17 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10019572
关键词：
Address Advisory Committees Algorithms Artificial Intelligence Benchmarking Binding Sites Bioinformatics Biological Biology Biomedical Research Categories Cognitive Complex Cryoelectron Microscopy Crystallization Data Data Set Descriptor Development Disease Docking Drug Design Drug Targeting Effectiveness Electron Microscopy Ensure FAIR principles Generations Human Intuition Ions Libraries Ligand Binding Ligands Machine Learning Methodology Methods Modeling Molecular Molecular Conformation Molecular Structure Nucleic Acid Binding Ontology Peptides Pharmaceutical Preparations Pharmacotherapy Proteins Protocols documentation Public Health Recommendation Reproducibility Research Resolution Resources Roentgen Rays Software Tools Standardization Stratification Structural Models Structural Protein Structure System Teaching Materials Techniques Training Uncertainty Update Validation X-Ray Crystallography base computational chemistry density drug discovery electron density experience improved inhibitor/antagonist machine learning algorithm macromolecule novel online resource simulation small molecule software development stem structured data three dimensional structure tool

项目摘要

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.

我们目前对疾病分子机制和基于结构的设计的理解用于治疗的药物，依赖于通过实验确定的蛋白质和其他物质的 3D 结构大分子与小分子配体复合。其中许多结构都直接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，或变构效应器。然而，基于结构的药物发现非常复杂且受到阻碍由实验偏差和现有实验配体方法的缺点识别，这通常会导致配体错误识别、缺失或错位。传播不仅消除了错误结构，而且增加了对结构数据的可访问性阻碍生物医学研究和药物发现的可重复性，但也转移了宝贵的资源注定会被限制在研究道路上。我们将利用丰富的经验来验证并精炼配体结合位点以生成将公开的配体参考库可在专门研究小分子相互作用的新网络资源上找到大分子。这些库可用于许多下游应用，例如药物设计、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的技术进步与现有工具相结合，创建了密度解释和公正、可重复配体的标准化方案鉴别。该管道不仅能够识别和建模未指定密度的配体片段，而且还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会出现认知偏差，因此它应该可以缓解最严重的问题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产生显着的有害连锁反应，我们将通过实验验证选择生物医学上重要的使用 X 射线对关键小分子进行可疑实验支持的结构晶体学或电子显微镜。