BACKGROUND Color vision is the ability to detect, distinguish, and analyze the wavelength distributions of light independent of the total intensity. It mediates the interaction between an organism and its environment from multiple important aspects. However, the physicochemical basis of color coding has not been explored completely, and how color perception is integrated with other sensory input, typically odor, is unclear. RESULTS Here, we developed an artificial intelligence platform to train algorithms for distinguishing color and odor based on the large-scale physicochemical features of 1,267 and 598 structurally diverse molecules, respectively. The predictive accuracies achieved using the random forest and deep belief network for the prediction of color were 100% and 95.23% ± 0.40% (mean ± SD), respectively. The predictive accuracies achieved using the random forest and deep belief network for the prediction of odor were 93.40% ± 0.31% and 94.75% ± 0.44% (mean ± SD), respectively. Twenty-four physicochemical features were sufficient for the accurate prediction of color, while 39 physicochemical features were sufficient for the accurate prediction of odor. A positive correlation between the color-coding and odor-coding properties of the molecules was predicted. A group of descriptors was found to interlink prominently in color and odor perceptions. CONCLUSIONS Our random forest model and deep belief network accurately predicted the colors and odors of structurally diverse molecules. These findings extend our understanding of the molecular and structural basis of color vision and reveal the interrelationship between color and odor perceptions in nature.

中文翻译：

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人工智能根据大规模化学信息数据破译颜色和气味感知的代码。


背景技术色视觉是检测、区分和分析与总强度无关的光的波长分布的能力。它从多个重要方面介导生物体与其环境之间的相互作用。然而，颜色编码的物理化学基础尚未被完全探索，并且颜色感知如何与其他感官输入（通常是气味）整合尚不清楚。结果在这里，我们开发了一个人工智能平台，用于训练分别基于 1,267 和 598 种结构不同分子的大规模物理化学特征来区分颜色和气味的算法。使用随机森林和深度置信网络预测颜色所达到的预测准确度分别为 100% 和 95.23% ± 0.40% (平均值 ± SD)。使用随机森林和深度置信网络预测气味的预测准确度分别为 93.40% ± 0.31% 和 94.75% ± 0.44%（平均值 ± 标准偏差）。 24 个物理化学特征足以准确预测颜色，而 39 个物理化学特征足以准确预测气味。预测分子的颜色编码和气味编码特性之间存在正相关。发现一组描述符在颜色和气味感知方面显着相互关联。结论我们的随机森林模型和深度信念网络准确地预测了结构不同的分子的颜色和气味。这些发现扩展了我们对色觉的分子和结构基础的理解，并揭示了自然界中颜色和气味感知之间的相互关系。