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几个稳定的晶体结构，并不是所有的都具有所需的性质。随着药物已经上市，一种新的、更稳定的晶体结构出人意料地产生了。这两种多态结构具有相同的化学组成，但在固体中的分子组装不同。分子在新晶体结构中的几何排列使其更稳定，更难溶，显著改变了药物的生物利用度。因此，数以千计的患者服用的剂量比预期的要小得多，这是制药行业的一大丑闻。药物开发的成功率非常低，在5000到10000种候选药物中，有十分之一有资格在人体上进行试验。药物开发从最初的发现到上市平均持续十年。正在进行的大流行表明，药物发现需要持续和实质性地加速。这种加速可以基于合理的方法，以一种新的连续方式对晶体结构进行分类[AK]。这篇论文[NS]警告了利托那韦的其他潜在情况以及计算方法如何帮助将风险降至最低。晶体结构预测(CSP)旨在识别给定分子的假定晶体堆积。CSP正在成为功能材料设计工作流程[P]不可或缺的一部分，包括药物开发[NR]。大多数CSP方法依赖于在所有可能的晶体填充的无限空间中进行随机搜索。Sally Price FRS(UCL)教授将CSP的最新技术描述为“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)通过计算生成的假定晶体，没有相似关系，而几乎总是只有不到10个实验可获得的晶体。模拟晶体是通过迭代优化得到的，它是决定预测晶体稳定性的势能的近似局部最小值。我们需要一种可靠的方法来建立预测结构之间的结构相似性，并揭示访问实验结构或相变的能量障碍。该PHD项目的目标是设计基于新的晶体不变量[AK，MK]的高效算法来指导在晶体药物空间中的搜索。建立可靠的晶体结构相似性将使我们了解潜在的可合成药物结构。题名/责任者：Required[AK]A.周期点集的等距分类[MK]M.Mosca，V.Kurlin.基于Voronoi的任意晶格之间的相似距离。水晶研究与技术，1900197,2020年。[NR]J.晶体结构预测正在从基础科学向应用技术转变。法拉第讨论2018,211,459-476。目前仍有多少利托那韦病例悬而未决？法拉第讨论2018,211,441-458.[P]A.Pulido等人.基于能量-结构-功能图的功能材料发现。《自然》2017,543,657-664。