Drugs have become an essential part of our lives due to their ability to improve people’s health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) after a protein target has been identified. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). Here, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1,834,362 compounds), was virtually screened to identify 48,232 potential active molecules using D2-MCS. Identified molecules were ranked to select 21 promising novel compounds for in vitro evaluation. Experimental validation confirmed six highly active hits (> 50% displacement at 10 µM and subsequent Ki determination) and an additional five medium active hits (> 25% displacement at 10 µM). Hence, D2-MCS provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%.

中文翻译：

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多重分类器系统可识别新型大麻素CB2受体配体

由于毒品具有改善人们健康和生活质量的能力，它们已成为我们生活中必不可少的一部分。然而，对于许多疾病而言，尚未获得批准的药物或现有药物具有不良副作用，这使得制药业努力寻找新药物和活性化合物。药物的开发是一个昂贵的过程，通常从识别蛋白质靶标后检测候选分子（筛选）开始。为此，为了减轻高成本，高性能筛选技术的使用已成为一个关键问题。因此，在过去的十年中，基于计算机的筛选（通常称为虚拟筛选或计算机筛选）的普及迅速增加。多种机器学习（ML）技术已与化学结构和物理化学特性结合使用，用于筛选目的，包括（i）简单分类器，（ii）集成方法，以及最近的（iii）多个分类器系统（MCS）。在这里，我们将MCS用于使用圆形指纹进行虚拟筛选（D2-MCS）。我们将我们的技术应用于从ChEMBL数据库获得的大麻素CB2配体数据集。使用D2-MCS对Enamine（1,834,362种化合物）的HTS收集物进行了虚拟筛选，以鉴定48,232个潜在的活性分子。对鉴定出的分子进行排名，以选择21种有前途的新型化合物进行体外评估。实验验证确认了六个高度活跃的命中（> 10 µM时位移> 50％，随后确定了Ki）和另外五个中等活跃的命中（> 10 µM时位移25％）。因此，D2-MCS对高活性化合物的命中率为29％，总命中率为52％。