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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >A ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge.
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A ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge.

机译：基于建模和推理的概率知识不精确的配体预测工具。

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摘要

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool.
机译：对配体的预测是由对生物分子如何识别其配体的基本愿望以及开发新药的商业需求推动的。当前大多数可用的软件系统非常复杂且使用起来很耗时。因此，开发简单有效的工具进行感兴趣化合物的初步筛选是一个吸引人的想法。在本文中，我们介绍了我们的工具，该工具可根据推理得出的结果，快速筛选可能的配体（底物或抑制剂），这些推理是根据过去实验得出的不精确的概率知识进行的。概率知识通过显示基本化合物结构的用户友好界面输入到系统中。根据获取的概率知识库查询特定化合物是否为底物的预测，并返回概率作为该预测的指示。该工具在已通过实验筛选出许多相似化合物但无法获得该组化合物所有可能成员信息的情况下特别有用。我们使用两个案例研究来演示如何使用该工具。

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