Cheminformatics and Machine Learning approaches for GPCR Computer-Aided Drug Design

该项目将专注于开发和应用化学信息学、机器学习(ML)和人工智能(AI)方法，用于G蛋白偶联受体(GPCRs)的计算机辅助药物设计(CADD)。G蛋白偶联受体是最大的细胞信号跨膜蛋白家族，可以受到大量化合物的调节。该项目将使用许多不同类型的蛋白质-配体相互作用数据中可用的信息来开发能够设计针对GPCRs的有效治疗化合物的模型。该项目将广泛使用从文献和专利中提取的公共生物活性数据，以及通过实验确定的GPCR型配体复合体的结构。您将使用各种计算化学和化学信息学技术，例如使用2D和3D方法的相似性评估、从头设计、用于性质预测的定量结构-活性关系、分子相互作用场和蛋白质-配体对接。您将致力于新方法的开发、评估和优化，并使用实验性的GPCR结构生物学、化学和药理学数据以及最先进的AI和ML技术，包括深度生成模型、卷积神经网络模型和强化学习。最终目标将是可应用于GPCR药物发现项目的技术和方法，作为设计-制造-测试-分析周期的一部分。

This project will focus on the development and application of cheminformatics, machine learning (ML), and Artificial Intelligence (AI) approaches for Computer-Aided Drug Design (CADD) for G Protein Coupled Receptors (GPCRs), the largest family of cell signalling transmembrane proteins that can be modulated by a plethora of chemical compounds. This project will use the information available in many heterogeneous types of protein-ligand interaction data to develop models that enable the design of efficacious therapeutic compounds targeting GPCRs. The project will make extensive use of public bioactivity data drawn both from the literature and patents, as well as experimentally determined structures of GPCR-ligand complexes. You will use a variety of computational chemistry and cheminformatics techniques such as similarity assessment using 2D and 3D approaches, de novo design, quantitative structure-activity relationships for property prediction, molecular interaction fields, and protein-ligand docking. You will work on the development, evaluation, and optimization of novel approaches augmenting with experimental GPCR structural biology, chemical, and pharmacological data and state-of-the-art AI and ML techniques, including deep generative models, convolutional neural network models, and reinforcement learning. The ultimate goal will be techniques and approaches that can be applied to GPCR drug discovery projects as part of the Design-Make-Test-Analyse cycle.