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Study of ligand-based virtual screening tools in computer-aided drug design

机译：计算机辅助药物设计中基于配体的虚拟筛选工具的研究

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Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates.One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes.In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.
机译：虚拟筛选是当今药物发现中的一项核心技术。可以测试数百万个分子，目的是仅选择最有希望的分子并通过实验对其进行测试。本文的主题是基于配体的虚拟筛选工具，该工具将现有的活性分子作为寻找新药物候选物的起点。本论文的一个目标是建立一个模型，该模型给出两种分子在生物学上相似的可能性，作为一种分子的功能或更多化学相似性分数。另一个重要的目标是评估不同的基于配体的虚拟筛选工具能够区分活性分子和非活性分子的程度。虚拟筛选工具的另一个标准集是它们在脚手架跳动中的适用性，即发现新的活性化学型。在工作的第一部分中，在筛选工具给出的抽象化学相似性评分与这两个分子在生物学上是相似的。这些结果有助于客观地确定哪些虚拟筛选命中要进行实验测试。当使用两个或更多工具时，这项工作还导致了一种新型的数据融合方法。在第二部分中，评估了五种基于配体的虚拟筛选工具，发现它们的性能普遍较差。提出了三个原因：基准集中的假阴性，不共享结合模式的活性分子和活性悬崖。在研究的第三部分中，提出了一种新颖的可视化和量化方法，用于评估虚拟筛选工具的支架跳跃能力。

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    Tiikkainen Pekka;

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  • 年度 2010
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  • 正文语种 fi
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