Machine Learning Methods for Predicting Phospholipidosis

磷脂病是细胞内过量脂肪物质（特别是磷脂）的积累，可发生在许多不同的器官和细胞类型中。已注意到对神经系统、淋巴系统、肝脏、肾脏、眼睛和肺部的影响。磷脂质病是制药行业非常关注的问题，尤其是在神经系统中，神经元中的磷脂质病会破坏细胞信号传导。由于药物的开发是一个非常昂贵的过程，因此能够在合成之前预测化学结构的不良反应非常重要。理想情况下，毒性预测应在新药设计的早期阶段进行，从而最大限度地减少在不安全或无效药物上浪费的费用和时间。在本项目中，我们将制作可能开发成药物的物质的磷脂质病诱导潜力的预测计算机模型。这些模型将比以前出现在科学文献中的模型更加复杂和准确。我们将使用的主要方法称为随机森林。森林是一组几百个决策树，每个决策树基本上是一个流程图。我们将训练他们学习现有药物的已知特性的模式，失败的候选人，以及他们诱发磷脂质病的倾向。然而，我们生成树的方式涉及计算机模拟骰子滚动。这将确保它们都是不同的，尽管基于相同的基本信息。然后决策树就像陪审团成员一样，投票决定每种新物质是否应该被归类为安全或不安全的。这里提出的工作是一个具有成本效益的项目，成功预测磷脂病诱导潜力的概率非常高。它使用最先进的基于计算机的化学和机器学习方法来解决当前设计和开发药物的主要问题。更一般地说，这项工作是在计算毒理学的发展领域的前沿。由于社会和政治原因，这几乎肯定会成为一个热点领域，因为人们越来越担心化学品和药物对环境和健康的影响，同时动物实验可能会越来越多地被淘汰。

Phospholipidosis is the accumulation of excessive quantities of fatty material (specifically phospholipids) within cells, which can occur in many different organs and cell types. Effects have been noted in the nervous system, lymphatic system, liver, kidneys, eyes and lungs. Phospholipidosis is of great concern to the pharmaceutical industry, especially in the context of the nervous system, where phospholipidosis in neurons can disrupt cell signalling.Since the development of medicines is such an enormously expensive process, it is extremely important to be able to predict adverse effects from chemical structure in advance of synthesis. Ideally, predictions of toxicity should be made at a very early stage in the design of new medicines, hence minimising the expense and time wasted on medicines that turn out to be unsafe or ineffective.In this project, we will produce predictive computer models of the phospholipidosis inducing potential of substances that might possibly be developed into medicines. These models will be substantially more sophisticated and accurate than the models that have previously appeared in the scientific literature. The main method we will use is called Random Forest. The forest is a set of several hundred decision trees , each of which is basically a flow diagram. We will train them to learn patterns in the known properties of existing medicines, and failed candidates, and their tendencies to induce phospholipidosis. However, the way in which we will generate the trees involves computer-simulated dice-rolling. This will ensure that they are all different, though based on the same underlying information. The decision trees then behave like jury members, voting on whether each new substance should be classed as safe or unsafe.The work proposed here is a cost-effective project with a very high probability of successfully predicting phospholipidosis inducing potential. It uses state-of-the art computer-based chemistry and machine learning methods to address a major current problem in designing and developing medicines. More generally, this work is at the cutting edge of the developing field of computational toxicology. For social and political reasons, this is almost certain to become a hot area as concerns about the environmental and health effects of chemicals and medicines mount, at the same time as animal experiments are likely to be increasingly phased out.