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Predicting chemical carcinogenesis using structural information only

机译:仅使用结构信息预测化学致癌作用

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This paper reports on the application of the Strongly Typed Evolutionary Programming System (STEPS) to the PTE2 challenge,which consists of predicting the carcinogenic activity of chemical compounds from their molecular structure and the outcomes of a number of laboratory analyses.most contestants so far have relied heavily on results of short term toxicity (STT) assays.Using both types of information made available,most models incorporate attributes that make them strongly dependent on STT results.Although such models may prove to be accurate and informative,the use of toxicological information requires time cost and in some cases substantial utilisation of laboratory animals.If toxicological information only makes explicit,properties implicit in the molecular structure of chemicals,then provided a sufficientaly expressive representation language,accurate solutions may be obtained from the structural informatin only.Such solutions may offer more tangible insight into the mechanistic paths and features that govern chemical toxicity as well as prediction based on virtual chemistry for ht euniverse of compounds.
机译:本文报道了强类型进化规划系统(STEPS)在PTE2挑战中的应用,该系统包括根据化合物的分子结构和许多实验室分析的结果来预测化合物的致癌活性。严重依赖于短期毒性(STT)分析的结果。使用提供的两种类型的信息,大多数模型都包含使它们强烈依赖于STT结果的属性。尽管此类模型可能被证明是准确且可提供信息的,但毒理学信息的使用需要时间成本,并且在某些情况下需要大量利用实验动物。如果毒理学信息仅使化学分子的结构明确,隐含特性,则提供了充分表达的表示语言,仅从结构信息中可以获得准确的解决方案。可能会提供有关机械性能的更切实的见解控制化学毒性的原理和特征以及基于虚拟化学原理对化合物的预测。

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