Exploring chemical space with computers: challenges and opportunities

机译：用计算机探索化学空间：挑战与机遇

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Summary form only given. Small molecules with at most a few dozen atoms play a fundamental role in organic chemistry and biology. They can be used as combinatorial building blocks for chemical synthesis, as molecular probes for perturbing and analyzing biological systems, and for the screening/design/discovery of new drugs. As datasets of small molecules become increasingly available, it becomes important to develop computational methods for the classification and analysis of small molecules and in particular for the prediction of their physical, chemical, and biological properties. We describe datasets and machine learning methods, in particular kernel methods, for chemical molecules represented by 1D strings, 2D graphs of bonds, and 3D structures. We demonstrate state-of-the-art results for the prediction of physical, chemical, or biological properties including the prediction of toxicity and anti-cancer activity. More broadly, we will discuss some of the challenges and opportunities for computer science, AI, and machine learning in chemistry.

机译：仅提供摘要表格。最多具有几十个原子的小分子在有机化学和生物学中起着基本作用。它们可以用作化学合成的组合构建基块，用于干扰和分析生物系统以及用于新药的筛选/设计/发现的分子探针。随着小分子数据集的日益可用，开发用于小分子分类和分析的计算方法，尤其是预测其物理，化学和生物学特性的计算方法就变得很重要。我们描述了由一维字符串，键的二维图和3D结构表示的化学分子的数据集和机器学习方法，尤其是内核方法。我们展示了预测物理，化学或生物学特性的最新结果，包括对毒性和抗癌活性的预测。更广泛地讲，我们将讨论计算机科学，人工智能和化学机器学习方面的一些挑战和机遇。

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《》|2005年|P.83|共1页
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Baldi; P.;
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中图分类工业技术;
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biochemistry; chemistry computing; learning (artificial intelligence); molecular biophysics; 1D strings; 2D bond graphs; 3D structures; biological systems; chemical molecules; chemical space; chemical synthesis; kernel methods; machine learning; molecular analysis; mo;

机译：生物化学;化学计算;学习（人工智能）;分子生物物理学;一维字符串;二维键图; 3D结构;生物系统;化学分子;化学空间;化学合成;核方法;机器学习;分子分析;分子式;

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Exploring chemical space with computers: challenges and opportunities

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