Although the development of computational models to aid drug discovery has become an integral part of pharmaceutical research, the application of these models often fails to produce the expected impact on productivity. One reason for this may be that the expected performance of many models is simply not supported by the underlying data, because of often neglected effects of assay and prediction errors on the reliability of the predicted outcome. Another significant challenge to realizing the full potential of computational models is their integration into prospective medicinal chemistry campaigns. This article will analyze the impact of assay and prediction error on model quality, and explore scenarios where computational models can expect to have a significant influence on drug discovery research.

中文翻译：

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健康的怀疑态度：评估现实模型的性能。

尽管开发有助于药物发现的计算模型已成为药物研究的组成部分，但这些模型的应用通常无法对生产率产生预期的影响。原因之一可能是基础模型数据根本不支持许多模型的预期性能，因为通常忽略分析和预测误差对预测结果可靠性的影响。实现计算模型的全部潜力的另一个重大挑战是将其集成到预期的药物化学研究中。本文将分析测定法和预测误差对模型质量的影响，并探讨一些场景，其中计算模型可望对药物发现研究产生重大影响。