Augmenting Molecular Property Prediction with AI and Chemical Calculation

利用人工智能和化学计算增强分子特性预测

• 批准号: 2318858
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2318858
• 关键词: Augmenting; Molecular; Property; Prediction; AI

Aims. We propose to apply cutting-edge AI algorithms to chemical simulation data, in order to extract key molecular level features that can be used to generate powerful statistical models for molecular property prediction using machine learning. The work has direct applications to improving molecular property prediction, which is of critical interest to the chemical industry. Companies such as Unilever, P&G, Akzonobel, GSK and Astrazeneca all have active research programs that require molecular property prediction via in silico methods. Background. Intricate organic solutes such as surfactants, macrocycles and biological compounds are becoming increasingly common place as industrial agents. These new modalities are increasingly required in a variety of industrial applications, for example: bio-surfactants for environmental remediation, novel polymeric materials for carbon capture and beyond-rule-of-5 drugs to modulate challenging biological targets in the pharmaceutical industry. Unfortunately, many of these new poly-functionalized organics exists in a chemical space where traditional guidelines on physical property optimization are inapplicable and predicting properties to aid design is currently extremely challenging [Nature Chem. Biol. 2016, 12, 1065-1074]. Computational modelling of poly-functionalized organic compounds is difficult because of their complex physical chemistry. Traditional "single-molecule" descriptors fail because they operate with simplified representations of molecular structure that ignore conformational degrees of freedom and the environment. Physics-based calculation methods provide a more realistic representation of the chemical environment and dynamics, but there are many properties that cannot be predicted with satisfactory accuracy using these methods alone.New Perspectives. Molecular dynamics (MD) simulations are a method to directly study the dynamics of a chemical system, leading to rich chemical insights. The featurization of a molecular dynamics trajectory will provide additional information for AI algorithms to build new powerful predictive models. An alternative approach is cDFT, a method that operates with functions describing local variations in the molecular density of a chemical system [Chem. Rev., 2015, 115, 6312-6356]. These density functions provide a wealth of information that is invaluable for interpreting chemical systems; this is evidenced by their use in predicting water sites on protein surfaces, fragment-based drug discovery, and molecular engineering. As with MD trajectories these density functions can be featurized and provided to AI methods to improve property predictions. Preliminary Evidence. One of us has recently shown accurate predictions of bioconcentration factors using a Convolution Neural Network (CNN) trained on 3D solute solvent correlation functions (computed by 3D RISM a cDFT method) [J. Phys.: Condens. Matter, 2018, 30, 32LT03]. This paper was selected as a research highlight by Physics World and Phys.org. Additionally, work from the DSP group has shown that permeability models performed better than the state-of-the-art tools for "small molecules" when they were built using descriptors computed with the 1D Reference Interaction Site Model [Mol. Pharmaceutics, 2015, 12, 3420-3432]. We have also demonstrated that the addition of features from chemical calculation can enhance the predictive accuracy of machine learning models for predicting the thermodynamics of sublimation and binding affinity. [J. Chem. Inf. Model., 2016, 56, 2162-2179; J. Chem. Inf. Model., 2018, 58, 1253-1265]

目标。我们建议将尖端的人工智能算法应用于化学模拟数据，以提取关键的分子水平特征，这些特征可用于生成强大的统计模型，用于使用机器学习进行分子特性预测。这项工作有直接的应用，以提高分子性质的预测，这是至关重要的兴趣，化学工业。联合利华、宝洁、阿克苏诺贝尔、葛兰素史克和阿斯利康等公司都有积极的研究计划，需要通过计算机方法预测分子性质。背景复杂有机溶质如表面活性剂、大环化合物和生物化合物作为工业试剂变得越来越常见。这些新的模式在各种工业应用中的需求越来越大，例如：用于环境修复的生物表面活性剂，用于碳捕获的新型聚合物材料以及用于调节制药行业中具有挑战性的生物靶标的5倍以上药物。不幸的是，这些新的多官能化有机物中的许多存在于化学空间中，在该化学空间中，关于物理性质优化的传统指导方针是不适用的，并且预测性质以辅助设计目前是极其具有挑战性的[Nature Chem.Biol.2016，12，1065-1074]。多官能化有机化合物的计算建模是困难的，因为它们复杂的物理化学。传统的“单分子”描述符失败，因为它们使用忽略构象自由度和环境的分子结构的简化表示。基于物理的计算方法提供了更真实的化学环境和动力学表示，但有许多属性无法单独使用这些方法以令人满意的精度进行预测。分子动力学（MD）模拟是一种直接研究化学系统动力学的方法，可以带来丰富的化学见解。分子动力学轨迹的特征化将为AI算法提供额外的信息，以构建新的强大的预测模型。另一种方法是cDFT，一种用描述化学系统分子密度局部变化的函数操作的方法[Chem. Rev.，2015，115，6312-6356]。这些密度函数提供了丰富的信息，这是非常宝贵的解释化学系统，这是证明了它们在预测蛋白质表面的水网站，基于片段的药物发现，和分子工程的使用。与MD轨迹一样，这些密度函数可以被特征化并提供给AI方法以改进属性预测。初步证据。我们中的一位最近使用在3D溶质溶剂相关函数（通过3D RISM cDFT方法计算）上训练的卷积神经网络（CNN）对生物浓缩因子进行了准确预测[J. Phys.：康登斯Matter，2018，30，32LT03].这篇论文被Physics World和Phys. org选为研究亮点。此外，DSP小组的工作表明，当使用用1D参考相互作用位点模型[Mol. Pharmaceutics，2015，12，3420-3432]。我们还证明，添加来自化学计算的特征可以提高机器学习模型预测升华热力学和结合亲和力的预测准确性。[J.化学信息模型，2016，56，2162-2179; J. Chem. Inf. Model.，2018年，58，1253-1265]