The Role of Machine Learning in Drug Design and Delivery

Jonathan P. Bernick
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引用次数: 4

Abstract

The applications discussed in this article typically use learning machines to perform supervised classification; i.e., the construction of algorithms to determine of the presence or absence of an exemplar in a class based on the values of the data comprising said exemplar. For example, a pharmacologist who wished to predict whether potential drug compounds were neurotoxic or not might use machine learning to construct a decision function from a set of drugs of known neurotoxicity or lack-thereof, and the function thus created would classify other drug compounds as belonging to the mutually exclusive classes of “neurotoxic” or “non-neurotoxic.”
机器学习在药物设计和交付中的作用
本文讨论的应用程序通常使用学习机来执行监督分类;即,基于包含所述示例的数据的值来确定类中示例的存在或不存在的算法的构造。例如,一位希望预测潜在药物化合物是否具有神经毒性的药理学家可能会使用机器学习从一组已知或缺乏神经毒性的药物中构建决策函数,从而创建的函数将其他药物化合物分类为属于相互排斥的“神经毒性”或“非神经毒性”类别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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