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In-silico ADME models: a general assessment of their utility in drug discovery applications.

机译：硅内ADME模型：对其在药物发现应用中的效用的一般评估。

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摘要

ADME prediction is an extremely challenging area as many of the properties we try to predict are a result of multiple physiological processes. In this review we consider how in-silico predictions of ADME processes can be used to help bias medicinal chemistry into more ideal areas of property space, minimizing the number of compounds needed to be synthesized to obtain the required biochemical/physico-chemical profile. While such models are not sufficiently accurate to act as a replacement for in-vivo or in-vitro methods, in-silico methods nevertheless can help us to understand the underlying physico-chemical dependencies of the different ADME properties, and thus can give us inspiration on how to optimize them. Many global in-silico ADME models (i.e generated on large, diverse datasets) have been reported in the literature. In this paper we selectively review representatives from each distinct class and discuss their relative utility in drug discovery. For each ADME parameter, we limit our discussion to the most recent, most predictive or most insightful examples in the literature to highlight the current state of the art. In each case we briefly summarize the different types of models available for each parameter (i.e simple rules, physico-chemical and 3D based QSAR predictions), their overall accuracy and the underlying SAR. We also discuss the utility of the models as related to lead generation and optimization phases of discovery research.
机译：ADME预测是一个极具挑战性的领域，因为我们尝试预测的许多属性是多种生理过程的结果。在这篇综述中，我们考虑如何利用ADME流程的计算机模拟预测来帮助将药物化学偏向属性空间的更理想区域，从而最大程度地减少为了获得所需的生物化学/物理化学特征而需要合成的化合物数量。尽管此类模型不够精确，无法替代体内或体外方法，但计算机内方法仍可以帮助我们了解不同ADME特性的潜在物理化学依赖性，因此可以给我们启发如何优化它们。文献中已经报道了许多全球计算机模拟ADME模型（即在大型，多样的数据集上生成）。在本文中，我们选择性地审查了每个不同类别的代表，并讨论了他们在药物发现中的相对效用。对于每个ADME参数，我们的讨论仅限于文献中最新，最具预测性或最有见地的示例，以突出显示当前的技术水平。在每种情况下，我们都简要总结了可用于每个参数的不同类型的模型（即简单规则，基于理化和基于3D的QSAR预测），它们的总体准确性和潜在的SAR。我们还讨论了与发现研究的线索生成和优化阶段相关的模型的实用性。

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