Bayesian models to accelerate antibacterial drug discovery

由一系列细菌引起的感染代表着一种重大的医疗需求，而制药业没有充分满足这一需求。结核分枝杆菌、ESKAPE细菌和选择性代理细菌构成了与全球健康相关的三类微生物，这在很大程度上是因为它们对现有治疗方法具有抵抗力。大多数新的抗菌药物是由经典的发现方法开发的，例如随机分析小分子集合对适当细菌的生长抑制作用。我们选择以不同的方式看待抗菌药物的发现，并寻求一种新的策略，利用贝叶斯模型来发现和优化更有效的小分子抗菌药。例如，我们将从这些随机“筛选”中产生的结核分枝杆菌数据视为一种计算学习机会。我们已经使用计算算法来分析测试的分子的哪些属性与活性和非活性是一致的。值得注意的是，这种方法产生了经过验证的结核分枝杆菌模型，这些模型预测了相对较高的成功率。因此，我们提出了这项技术的两个重要扩展：1)从我们的模型中产生的三个最有希望的抗结核活性物质的优化和2)这种贝叶斯方法的创建和验证，以发现针对ESKAPE和精选试剂细菌的新活性物质，这些活性物质将随后进行优化。这些优化过程将为具有巨大潜力的分子提供新的治疗方法。

Infections caused by a range of bacteria represent a significant medical need that is not being sufficiently addressed by the pharmaceutical industry. M. tuberculosis, the ESKAPE bacteria, and Select Agent bacteria constitute three classes of microbes that are relevant to global health in large part because of their resistance to available therapeutics. Most new antibacterials are developed by classical discovery methodologies, such as randomly assaying small molecule collections for growth inhibition ofthe appropriate bacterium. We have chosen to look at antibacterial drug discovery differently and sought a novel strategy utilizing Bayesian models to discover and optimize small molecule antibacterials that is more efficient. For example, we viewed the M. tuberculosis data generated from these random "screens" as a computational learning opportunity. We have used computational algorithms to analyze what attributes ofthe molecules tested are consistent with activity and inactivity. Significantly, this approach yielded validated models for M. tuberculosis that have predicted actives with comparatively high rates of success. Thus, we propose two important extensions of this technology: 1) the optimization ofthe three most promising antitubercular actives arising from our models and 2) the creation and validation of this Bayesian methodology to uncover novel actives against each ofthe ESKAPE and Select Agent bacteria, which will be subsequently optimized. These optimization processes will afford molecules with significant potential as novel therapeutics.