Schmuker Michael, Schneider Gisbert
Beilstein-Endowed Chair for Cheminformatics, Johann Wolfgang Goethe-Universität, Siesmayerstrasse 70, 60323 Frankfurt, Germany.
Proc Natl Acad Sci U S A. 2007 Dec 18;104(51):20285-9. doi: 10.1073/pnas.0705683104. Epub 2007 Dec 10.
The chemical sense of insects has evolved to encode and classify odorants. Thus, the neural circuits in their olfactory system are likely to implement an efficient method for coding, processing, and classifying chemical information. Here, we describe a computational method to process molecular representations and classify molecules. The three-step approach mimics neurocomputational principles observed in olfactory systems. In the first step, the original stimulus space is sampled by "virtual receptors," which are chemotopically arranged by a self-organizing map. In the second step, the signals from the virtual receptors are decorrelated via correlation-based lateral inhibition. Finally, in the third step, olfactory scent perception is modeled by a machine learning classifier. We found that signal decorrelation during the second stage significantly increases the accuracy of odorant classification. Moreover, our results suggest that the proposed signal transform is capable of dimensionality reduction and is more robust against overdetermined representations than principal component scores. Our olfaction-inspired method was successfully applied to predicting bioactivities of pharmaceutically active compounds with high accuracy. It represents a way to efficiently connect chemical structure with biological activity space.
昆虫的化学感官已经进化到能够对气味进行编码和分类。因此，它们嗅觉系统中的神经回路可能采用了一种有效的方法来对化学信息进行编码、处理和分类。在这里，我们描述了一种处理分子表征并对分子进行分类的计算方法。这种三步法模仿了在嗅觉系统中观察到的神经计算原理。第一步，原始刺激空间由“虚拟受体”进行采样，这些虚拟受体通过自组织映射在化学拓扑上进行排列。第二步，来自虚拟受体的信号通过基于相关性的侧向抑制去相关。最后，在第三步中，嗅觉感知由机器学习分类器进行建模。我们发现，第二阶段的信号去相关显著提高了气味分类的准确性。此外，我们的结果表明，所提出的信号变换能够进行降维，并且比主成分得分对超定表征更具鲁棒性。我们受嗅觉启发的方法成功地应用于高精度预测药物活性化合物的生物活性。它代表了一种将化学结构与生物活性空间有效连接的方法。
