Research Initiation Award: Exploring Class A G-Protein Coupled Receptors (GPCRs)-Ligand Interaction through Machine Learning Approaches

HBCU-UP的研究启动奖为STEM教师开展研究活动提供支持，以提高他们的研究能力和有效性，并帮助加强HBCU的研究和教学。这项授予鲍伊州立大学的奖项有可能利用机器学习(ML)方法识别新药，并为学生提供跨学科培训。该项目的重点是确定A类G蛋白偶联受体(GPCRs)的结合配体，这是所有药物的靶标。该项目旨在为药物发现做出重大贡献，并为HBCU本科生提供新兴领域的培训。该项目旨在开发新的分子模型和机器学习方法管道，以快速和准确地建立单个抑制剂/GPCR复合体的3D结构模型，而不需要实验结构。将使用ML工具从所有复合体中提取结合特征，并将使用GPCR蛋白质-配体复合体生成深度学习模型，以识别针对A类GPCRs的新分子。该项目有可能开发有价值的工具，以更低的成本和更高的效率优化新药的效力和选择性。该项目将为使用ML技术的跨学科学生创造新的机会，并有助于发展更多样化的工作队伍。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

HBCU-UP’s Research Initiation Awards provide support for STEM faculty to pursue research activities to further their research capabilities and effectiveness and help enhance research and teaching at HBCUs. This award to Bowie State University has the potential to identify new drugs using machine learning (ML) methods and provide interdisciplinary training to students. The project focuses on identifying bonding ligands for Class A G protein-coupled receptors (GPCRs), which are the targets for 40-50% of all pharmaceuticals. The project aims to make significant contributions to drug discovery and provide training in emerging fields for HBCU undergraduate students. This project seeks to develop new molecular models and a machine learning approach pipeline for rapidly and accurately building 3D structural models of individual inhibitor/GPCR complexes without experimental structures. ML tools will be used to extract binding features from all complexes and deep learning models will be generated using GPCR protein-ligand complexes to identify novel molecules that target Class A GPCRs. The project has the potential to develop valuable tools for optimizing the potency and selectivity of new drugs at lower costs and higher efficiency. The project will build new opportunities for students in interdisciplinary science using ML techniques and contribute to developing a more diverse workforce.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.