QSAR datasets - Meta-QSAR

We extracted 2,219 protein targets from ChEMBL with a diverse number of drug-like chemical compounds, ranging from 30 to about 6,000, each target resulting in a dataset with as many examples as compounds. The datasets were originally used in (Olier et al. Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Machine Learning, 2018, 107 (1), 285-311). Chemical compounds were intrinsically described using a standard fingerprint representation (as it is the most commonly used in QSAR learning), where the presence or absence of a particular molecular substructure in a molecule (e.g. methyl group, benzene ring) is indicated by a Boolean variable. Specifically, we used the RDKit to calculate the 1024 bits FCFP4 fingerprint representation, which is one of the extended-connectivity fingerprints (Rogers and Hahn, 2010) for molecular characterisation. Each dataset consisted of 1,024 input binary variables, one for each fingerprint bit, and one floating-point output variable which represented the chemical compound activities against the target. We used IC50 values, inhibitory drug concentrations at 50%. IC50 value states the concentration of the drug compound that is required to block or inhibit 50% of the proteins. This response data has been normalised by taking the negative log of the drug concentrations that inhibited 50% of a target (pXC50).我们从ChEMBL数据库中提取了2219个蛋白质靶点，每个靶点对应30至约6000不等数量的类药化合物，各靶点对应的数据集样本数与该靶点的类药化合物总量一致。该数据集最初被应用于Olier等人发表于《Machine Learning》2018年第107卷第1期（第285-311页）的研究《Meta-QSAR：元学习在药物设计与发现中的大规模应用》。 类药化合物的特征采用标准指纹表征方式进行描述——这是定量构效关系（Quantitative Structure-Activity Relationship, QSAR）学习中最常用的表征手段，通过布尔变量标记分子中特定分子子结构（如甲基、苯环）的存在与否。具体而言，我们使用RDKit工具计算1024位的FCFP4指纹表征，该表征属于扩展连接指纹（extended-connectivity fingerprints）的一类，用于分子特征刻画（Rogers与Hahn，2010）。 每个数据集包含1024个输入二进制变量（与每个指纹位一一对应），以及一个浮点型输出变量，用于表征类药化合物针对对应靶点的活性。我们采用半最大抑制浓度（half maximal inhibitory concentration, IC50）作为活性指标，即可抑制50%靶蛋白活性的药物化合物浓度。该响应数据已通过对抑制50%靶点活性的药物浓度取负对数完成标准化处理，所得标准化值记为pXC50。