Deep Neural Networks for Pose Validation, Affinity Prediction, and Input Attribution

用于姿势验证、亲和力预测和输入归因的深度神经网络

• 批准号: 2108078
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2108078
• 关键词: Deep; Neural; Networks; Pose; Validation

Our inability to rapidly design small molecules that bind strongly to a macromolecule is a major bottleneck for understanding biological science. It typically still takes many years and large inter-disciplinary teams to create one such molecule. High-throughput facilities such as XChem at Diamond Light Source now enable the trivial identification of weakly binding small molecules (fragments). For this resource to be of benefit to biology we must now streamline the process of converting weak binders to strong binders. AI methods, being actively developed by BenevolentAI (BAI), have shown great promise in streamlining this process. Technical summary: Currently decision making is subjective and time-consuming, even when an optimal decision is possible. Astonishingly, no freely-available computational methods exist that can provide automated solutions for this. We therefore propose a DPhil working actively with AI and Machine Learning Experts at BAI and the breakthrough XChem technology to develop such tools: Collating and generating large datasets of protein-fragment complexes Machine Learning (e.g. CNNs) for classifiers for determining if a ligand is present Artificial Intelligence to make use of classifiers to make automated decisions Project outcomes: This project will produce computational techniques and datasets that will enable improvements in data driven biology. Further, the work would greatly speed up the development of chemical probes used to study biological processes: such probes invariably have great impact as they enable fine-grained, systematic studies of phenotypic effects of specific protein targets. They would work in close-partnership with BAI, including at minimum 3 months spent on site.

我们无法快速设计出与大分子紧密结合的小分子，这是理解生物科学的一个主要瓶颈。通常情况下，仍需要多年的时间和庞大的跨学科团队来创造一个这样的分子。高通量设备，如钻石光源的XChem，现在能够实现对弱结合小分子(碎片)的微不足道的识别。为了使这种资源对生物学有益，我们现在必须简化将弱粘结剂转化为强粘结剂的过程。由慈善人工智能(BAI)积极开发的人工智能方法在简化这一过程方面显示出了巨大的前景。技术摘要：目前的决策是主观和耗时的，即使有可能做出最优决策。令人惊讶的是，没有免费可用的计算方法可以为这一问题提供自动化解决方案。因此，我们建议DPhil积极与BAI和机器学习专家以及突破性的XChem技术合作，以开发这样的工具：整理和生成蛋白质-片段复合体的大型数据集机器学习(例如CNN)，用于确定是否存在配体的分类器人工智能利用分类器做出自动化决策项目结果：该项目将产生计算技术和数据集，将使数据驱动生物学的改进成为可能。此外，这项工作将大大加快用于研究生物过程的化学探针的开发：这种探针总是有很大的影响，因为它们能够对特定蛋白质靶标的表型效应进行细粒度、系统的研究。他们将与白密切合作，包括至少在现场工作3个月。