Active Learning for Computational Polymorph Landscape Analysis

计算多晶型景观分析的主动学习

• 批准号: EP/S015418/1
• 负责人: Graeme Day
• 金额: $ 31.99万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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=EP%2FS015418%2F1
• 关键词: Active; Learning; Computational; Polymorph; Landscape

The proposed research will develop advanced computational methods for predicting the possible crystal structures of drug-like molecules. The work is motivated by the importance of anticipating the occurrence of polymorphism, where a molecule can crystallise in more than one crystal structure, depending on the conditions used for its crystallisation. In the context of pharmaceutical materials, we must know when polymorphs exist that we have not yet characterised. These present a risk related to property control; a change in crystal structure can dramatically alter important properties of a crystalline drug, affecting its processing, tabletting and bioavailability. Hence, there has been a huge investment in crystal structure prediction methods. Predicted structures could guide experimental screening - where to focus effort and, in the long run, what experimental variables to vary to maximise likelihood of isolating new structures. Structure prediction has progressed impressively but still not made the expected impact on assessing risk. A root cause is the problem of over-prediction. Current methods always predict many competing crystal forms, most of which are never observed. Accordingly all candidate drug molecules appear to have significant uncertainly as to expected extent of polymorphism and this adversely impacts risk analysis. The root of the problem is that the underlying lattice energy surface, on which local minima represent possible structures, is extremely complex and current methods for predicting polymorphism do not provide a sufficiently detailed description of this energy surface. We will develop the use of statistical learning methods to guide crystal structure calculations to efficiently map out the global features of lattice energy surfaces in a way that is not possible using current computational methods. Two lines of study are proposed: to improve the fidelity of energetic assessment and, more importantly, to map the energy landscape of structures more globally. A starting point is to develop advanced statistical learning methods for correcting approximate computational models that are used for assessing lattice energies of predicted crystal structures. Our goal is to reduce the uncertainty in ranking of predicted structures at a controlled computational cost. We will then move to a completely unexplored problem: learning more detailed features of the lattice energy surface, such as the depth, shape and connectivity of energy basins. Key to this work is the development of multi-fidelity (multiple models of known accuracy and computational cost) and multi-objective Bayesian optimisation approaches to make use of the hierarchical of energy models (a series of approximate energy models with known, ordered accuracy) used in crystal structure prediction.The objective is to judge the thermodynamic robustness and kinetic accessibility of individual predicted crystal structures and address the polymorphism over-prediction problem. This is completely new in the area and can be transformative in guiding experimental screening.Thus, the vision is that active learning methods will guide the computer simulations that, in turn, will provide guidance to experimental polymorph screening.

拟议的研究将开发先进的计算方法来预测药物样分子的可能晶体结构。这项工作的动机是预测多态性发生的重要性，其中一个分子可以结晶在一个以上的晶体结构，这取决于其结晶所使用的条件。在制药材料方面，我们必须知道何时存在我们尚未表征的多晶型物。这些存在与性质控制相关的风险;晶体结构的变化可显著改变晶体药物的重要性质，影响其加工、压片和生物利用度。因此，在晶体结构预测方法上进行了巨大的投资。预测的结构可以指导实验筛选-在哪里集中精力，以及从长远来看，改变什么实验变量以最大限度地提高分离新结构的可能性。结构预测已经取得了令人印象深刻的进展，但仍然没有取得预期的影响，评估风险。一个根本原因是过度预测的问题。目前的方法总是预测许多竞争的晶体形式，其中大多数从未被观察到。因此，所有候选药物分子似乎对预期的多态性程度具有显著的不确定性，这对风险分析产生了不利影响。问题的根源在于潜在的晶格能表面（局部最小值代表可能的结构）是极其复杂的，并且用于预测多晶型的当前方法没有提供对该能表面的足够详细的描述。我们将开发使用统计学习方法来指导晶体结构计算，以有效地绘制出晶格能量表面的全局特征，这是使用当前计算方法不可能实现的。提出了两条研究路线：提高能量评估的保真度，更重要的是，更全球化地绘制结构的能量景观。一个出发点是开发先进的统计学习方法，用于校正用于评估预测晶体结构的晶格能的近似计算模型。我们的目标是在控制计算成本的情况下，减少预测结构排序的不确定性。然后，我们将转向一个完全未探索的问题：学习晶格能量表面的更详细的特征，例如能量盆地的深度，形状和连通性。这项工作的关键是发展多保真度（已知精度和计算成本的多个模型）和多目标贝叶斯优化方法，以利用能量模型的层次结构（一系列近似的能量模型，精确度）用于晶体结构预测。目标是判断各个预测晶体结构的热力学稳健性和动力学可及性，并解决多态性过度预测问题这在该领域是全新的，并且可以在指导实验筛选方面具有变革性。因此，我们的愿景是，主动学习方法将指导计算机模拟，从而为实验多晶型筛选提供指导。