A high-throughput FEP pipeline for optimisation of hits into leads for novel drug targets

高通量 FEP 管道，用于优化新药物靶点先导化合物的命中

• 批准号: 2581429
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2581429
• 关键词: throughput; FEP; pipeline; optimisation; hits

Free Energy Perturbation (FEP) methods are increasingly used in industry to guide potency optimisation of preclinical candidate compounds. FEP methods are well suited to optimise compounds for novel drug targets amenable to structure-based drug design as they can deliver high accuracy potency predictions without requiring a prior training set of active compounds. However the performance of FEP varies across target classes (e.g. kinases, GPCRs) and can be affected by the choice of the FEP implementation. Currently significant technical expertise is required to deploy FEP effectively in an industrial R&D setting.The goal of this research is to develop an optimised open source FEP implementation for robust performance across broad range of drug target classes. Systematic comparison of leading FEP implementations (available via e.g. Amber, Gromacs, OpenMM) will be facilitated by development of easy to use automated pipelines for high throughput FEP studies using the BioSimSpace framework for biomolecular simulation (www.biosimspace.org). Protein-ligand binding free energies will be computed using high performance computing and cloud computing resources for a diverse dataset of drug targets of current interest to Evotec. Lessons learned from these large scale validation studies will drive the implementation of novel algorithms in our in-house FEP software SOMD (www.siremol.org) to improve the accuracy, speed and reliability of FEP. Through periods of placement at Evotec the student will deploy the new software onto internal computing resources and help molecular modellers apply the methodology to real world drug discovery projects.

自由能微扰(FEP)方法在工业中越来越多地用于指导临床前候选化合物的效力优化。FEP方法非常适合于针对基于结构的药物设计的新药物靶点优化化合物，因为它们可以提供高精度的效力预测，而不需要事先训练活性化合物集。然而，FEP的性能因目标类(例如，Kase、GPCRs)而异，并且可能受到FEP实现的选择的影响。目前，在工业研发环境中有效地部署FEP需要重要的技术专业知识。本研究的目标是开发一个优化的开源FEP实现，以在广泛的药物靶标类别中实现稳健的性能。通过使用BioSimSpace生物分子模拟框架开发易于使用的自动化管道进行高通量FEP研究，将有助于对领先的FEP实施进行系统比较(可通过Amber、Gromacs、OpenMM获得)(www.Biosimspace.org)。对于Evotec目前感兴趣的药物靶标的不同数据集，将使用高性能计算和云计算资源计算蛋白质-配体结合自由能。从这些大规模验证研究中吸取的经验教训将推动我们的内部FEP软件SOMD(www.siremol.org)实施新的算法，以提高FEP的准确性、速度和可靠性。通过在Evotec的实习，学生将把新软件部署到内部计算资源上，并帮助分子模型师将该方法应用于现实世界的药物发现项目。