Targeted Design of Small Molecules using Advanced Machine Learning Approaches

使用先进的机器学习方法进行小分子的靶向设计

• 批准号: 2599699
• 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: 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-2599699
• 关键词: Targeted; Design; Small; Molecules; using

De novo therapeutic design aims to generate new molecules, or enhance existing molecules, with desirable properties. Traditionally, this process is carried out by medicinal chemists, who leverage their knowledge of a given target's structure to design a molecule with high target binding affinity, off-target selectivity and low synthetic cost, among many other requirements. However, these properties often directly compete, making the generation of novel drugs a costly and time-intensive process - a recent study of the research and development processes placed the median cost of developing a drug at 985 million USD, and found the average time required to be just over 8 years before clinical trials could begin. Machine learning (ML) algorithms, particularly deep neural networks, present a promising alternative to traditional molecule design techniques, and aim to reduce the price and time demands of drug manufacturing. To apply machine learning to molecule design, molecular data must first be encoded into a readable format. A range of different approaches have been used for this; some rely on low-dimensional representations of molecules, which reduces computational demand and allows the use of natural language processing models. More recently, however, several studies have shown success employing a structure-based approach, incorporating 3D information about the target or known-actives (molecules known to bind to the target) to design candidate molecules with complementary structures to the binding site in question. This project, which is partially funded by IBM Research, falls within the EPSRC artificial intelligence and robotics research area. The main project objective is to contribute to the growing body of research surrounding computer aided drug design. Practically, this contribution could take many forms, but the initial aim will be to further develop an existing deep generative model capable of incorporating 3D structural information about the target of choice to produce candidate drug molecules. Once generated, structure-based virtual screening methods will be used to assess the quality of the candidates produced and hence also the model.Practically speaking, I will initially be using a model recently published by IBM research. In this model, the target's structural information is encoded through first representing the 3D structure by voxels (units of graphic information that define a point in three-dimensional space) of the secondary structure element (SSE) density. This approach ensures the structural information of the protein is preserved in a scale-free manner. This project falls within the EPSRC artificial intelligence and robotics research area.

从头治疗设计旨在产生具有所需性质的新分子或增强现有分子。传统上，这个过程是由药物化学家进行的，他们利用他们对给定靶标结构的知识来设计具有高靶标结合亲和力、脱靶选择性和低合成成本的分子，以及许多其他要求。然而，这些特性通常直接竞争，使得新药的产生是一个昂贵且耗时的过程-最近对研发过程的研究将开发药物的平均成本定为9.85亿美元，并发现在临床试验开始之前所需的平均时间仅为8年多一点。机器学习（ML）算法，特别是深度神经网络，为传统的分子设计技术提供了一种有前途的替代方案，旨在降低药物制造的价格和时间需求。为了将机器学习应用于分子设计，分子数据必须首先编码为可读格式。一系列不同的方法已被用于此;一些依赖于分子的低维表示，这降低了计算需求，并允许使用自然语言处理模型。然而，最近的几项研究已经成功地采用了基于结构的方法，结合了关于靶标或已知活性物质（已知与靶标结合的分子）的3D信息，以设计具有与所讨论的结合位点互补结构的候选分子。该项目由IBM Research提供部分资金，属于EPSRC人工智能和机器人研究领域的福尔斯。主要的项目目标是为计算机辅助药物设计的研究做出贡献。实际上，这种贡献可以采取多种形式，但最初的目标将是进一步开发现有的深度生成模型，该模型能够整合有关选择目标的3D结构信息，以产生候选药物分子。一旦生成，基于结构的虚拟筛选方法将用于评估所产生的候选人的质量，因此也是模型。实际上，我将首先使用IBM研究最近发布的模型。在该模型中，目标的结构信息通过首先由二级结构元素（SSE）密度的体素（定义三维空间中的点的图形信息的单位）表示3D结构来编码。这种方法确保蛋白质的结构信息以无标度的方式保留。该项目属于EPSRC人工智能和机器人研究领域的福尔斯。