Predicting the safety of a drug from preclinical data is a major challenge in drug discovery, and progressing an unsafe compound into the clinic puts patients at risk and wastes resources. In drug safety pharmacology and related fields, methods and analytical decisions known to provide poor predictions are common and include creating arbitrary thresholds, binning continuous values, giving all assays equal weight, and multiple reuse of information. In addition, the metrics used to evaluate models often omit important criteria and models' performance on new data are often not assessed rigorously. Prediction models with these problems are unlikely to perform well, and published models suffer from many of these issues. We describe these problems in detail, demonstrate their negative consequences, and propose simple solutions that are standard in other disciplines where predictive modelling is used.

中文翻译：

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通过减少不良的分析实践来改善药物安全性预测

根据临床前数据预测药物的安全性是药物开发中的一项重大挑战，将不安全的化合物推向临床将使患者处于危险之中并浪费资源。在药物安全药理学和相关领域中，已知提供较差预测的方法和分析决策很常见，包括创建任意阈值，对连续值进行分箱，赋予所有测定相同的权重以及信息的多次重用。另外，用于评估模型的指标通常会忽略重要的标准，并且模型在新数据上的性能通常不会得到严格评估。具有这些问题的预测模型不太可能表现良好，而已发布的模型则遭受许多此类问题的困扰。我们将详细描述这些问题，并说明其负面影响，