BIOGEM Istituto di Ricerche Genetiche "G. Salvatore", 83031 Ariano Irpino, Italy.
ABBVIE Biotherapeutics, Redwood City, CA 94063, USA.
Bioinformatics. 2021 Jun 16;37(10):1420-1427. doi: 10.1093/bioinformatics/btaa968.
The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples.
Since the absence of negative samples does not allow standard methods for automatic selection of hyperparameters, we propose a novel approach for hyperparameter selection of the kernel in One Class Gaussian Processes. We compare our methods with state-of-the-art approaches on benchmark datasets and then show its application to druggability prediction of oncology drugs. Our score reaches an AUC 0.90 on a set of clinical trial targets starting from a small training set of 102 validated oncology targets. Our score recovers the majority of known drug targets and can be used to identify novel set of proteins as drug target candidates.
The matrix of features for each protein is available at: https://bit.ly/3iLgZTa. Source code implemented in Python is freely available for download at https://github.com/AntonioDeFalco/Adaptive-OCGP.
Supplementary data are available at Bioinformatics online.
在过去的几十年里，药物研发的成本大幅增加，每投入 10 亿美元用于研发的新药数量每年减少一半或更少。目标的选择和优先级排序是药物发现中最具影响力的决策之一。在这里，我们提出了一种用于药物靶点优先级排序的高斯过程模型，该模型将其视为仅具有正例和未标记样例的学习问题。
由于没有负例样本，不允许使用标准方法自动选择核函数的超参数，因此我们提出了一种用于单类高斯过程核函数超参数选择的新方法。我们在基准数据集上比较了我们的方法和最先进的方法，然后展示了它在肿瘤药物可药性预测中的应用。我们的评分在一组临床试验靶点上达到了 0.90 的 AUC，起始训练集为 102 个已验证的肿瘤靶点。我们的评分可以恢复大多数已知的药物靶点，并可用于识别新的蛋白质组作为药物靶点候选物。
每个蛋白质的特征矩阵可在以下网址获得：https://bit.ly/3iLgZTa。在 Python 中实现的源代码可在以下网址免费下载：https://github.com/AntonioDeFalco/Adaptive-OCGP。
补充数据可在生物信息学在线获得。
