AI-based exploration of crystal spaces to accelerate drug discovery

基于人工智能的晶体空间探索加速药物发现

• 批准号: 2599484
• 金额: --
• 依托单位: University of Liverpool
• 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-2599484
• 关键词: AI; based; exploration; crystal; spaces

The commercial anti-viral drug Ritonavir has several stable crystalline structures and not all of them have desired properties. With the drug already in the market, a new and more stable crystalline structure was unexpectedly produced. The two polymorphic structures have the same chemical composition, but differ in molecule assembly in the solid state. The geometric arrangement of molecules in the new crystalline structure made it more stable and less soluble, modifying significantly bio-availability of drugs. Hence thousands of patients took much smaller than expected doses, which was a big scandal in the pharmaceutical industry. Success rates for drug development are very low and a tenth of the 5 to 10 thousand drug candidates qualifies for test in humans. Drug developments from initial discovery to marketplace last a decade on average. The ongoing pandemic shows that drug discovery needs to be continuously and substantially accelerated. This acceleration can be based on justified methods to classify crystal structures in a new continuous way [AK]. The paper [NS] warns about other potential Ritonavir situations and how computational methods can assist minimising risk.Crystal Structure Prediction (CSP) aims to identify putative crystal packing for a given molecule. CSP is becoming integral part of the functional materials design workflow [P], including drug development [NR]. Most CSP methods rely on a random search in the infinite space of all possible crystal packings. Prof Sally Price FRS (UCL) has described the state-of-the-art in CSP as `embarrassment of over-prediction', because modern CSP tools output too many (thousands or millions) computationally generated putative crystals, without similarity relations, while there are almost always only less than 10 experimentally accessible crystals.Simulated crystals are obtained by an iterative optimisation as approximate local minima of a potential energy that determines the stability of the predicted crystals. We need a reliable way to establish structural similarities between predicted structures and to reveal energy barriers for accessing experimental structures or phase transformations.The aim of this PhD project is to design efficient algorithms based on new crystal invariants [AK, MK] to guide a search in a space of crystalline drugs. Establishing reliable similarities of crystal structures will inform us about potentially synthetically accessible drug structures. References[AK] O.Anosova, V.Kurlin. An isometry classification of periodic point sets.[MK] M.Mosca, V.Kurlin. Voronoi-based similarity distances between arbitrary crystal lattices. Crystal Research and Technology, 1900197, 2020.[NR] J.Nyman, S.M.Rutzel-Edens. Crystal Structure Prediction is changing from basic science to applied technology. Faraday Discussions 2018, 211, 459-476.[NS] M.A.Neumann, J. van de Streek. How many ritonavir cases are still out there? Faraday Discussions 2018, 211, 441-458.[P] A.Pulido et al. Functional materials discovery using energy-structure-function map. Nature 2017, 543, 657-664.

商业抗病毒药物利托那韦具有多种稳定的晶体结构，并非所有这些结构都具有所需的特性。随着药物已经进入市场，意外产生了一种新的，更稳定的结晶结构。两种多态性结构具有相同的化学成分，但在固态下的分子组件方面有所不同。新的晶体结构中分子的几何布置使其更稳定，溶解度较低，从而改变了药物的生物可用性。因此，成千上万的患者服用的剂量要小得多，这在制药行业是一个很大的丑闻。药物开发的成功率非常低，在5至10,000名候选药物中的十分之一中有资格参加人类的测试。从最初发现到市场的药物发展平均持续十年。正在进行的大流行表明，药物发现需要连续和大幅加速。这种加速度可以基于以新的连续方式对晶体结构进行分类的合理方法[AK]。该论文警告说，其他潜在的利托那韦情况以及计算方法如何有助于最大程度地降低风险。晶体结构预测（CSP）旨在确定给定分子的假定晶体包装。 CSP正在成为功能材料设计工作流[P]的组成部分，包括药物开发[NR]。大多数CSP方法都依赖于所有可能的晶体包装的无限空间中的随机搜索。 Sally Price FRS教授（UCL）将CSP中的最新设备描述为“过度预测的尴尬”，因为现代CSP工具输出了太多（数千或数百万）的计算产生的假定晶体，而没有相似性关系，而几乎总是有少于10个实验性地访问的晶体，该晶体的稳定性几乎是稳定的，即稳定的稳定性，均匀的稳定性是一定的。晶体。我们需要一种可靠的方法来在预测的结构之间建立结构相似性，并揭示访问实验结构或相变的能量障碍。该博士学位项目的目的是设计基于新的水晶不变剂[AK，MK]的有效算法，以指导在结晶药物空间中搜索的搜索。建立可靠的晶体结构相似性将为我们告知潜在的合成药物结构。参考[AK] O.Anosova，V.Kurlin。周期点集的等轴测分类。[MK] M.Mosca，V.Kurlin。任意晶体晶格之间的基于Voronoi的相似性距离。水晶研究与技术，1900197年，2020年。[NR] J.Nyman，S.M.Rutzel-Edens。晶体结构预测从基础科学变成了应用技术。法拉第讨论2018，211，459-476。[NS] M.A.Neumann，J。Vande Streek。还有多少例利托那韦案件？法拉第讨论2018，211，441-458。[P] A.Pulido等。使用能量结构功能图的功能材料发现。自然2017，543，657-664。