Pacific Northwest National Laboratory , Richland , Washington 99352 , United States.
Anal Chem. 2020 Jan 21;92(2):1720-1729. doi: 10.1021/acs.analchem.9b02348. Epub 2020 Jan 6.
Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g., mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e., without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (/, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large data set of structures with / labels. Next, property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller data sets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.
全面而明确地识别复杂样品中的小分子将彻底改变我们对代谢物在生物系统中作用的认识。现有的和新兴的技术已经能够测量复杂混合物中分子的化学性质，并且它们的灵敏度足以分辨甚至是立体异构体。尽管有了这些实验上的进步，但小分子的识别仍然受到以下两个因素的限制：（i）化学参考库（例如，质谱、碰撞截面和其他可测量的性质库）仅代表已知分子的<1%，限制了可能的识别数量；（ii）缺乏直接从实验特征生成候选匹配的方法（即，没有库）。为此，我们开发了一种变分自动编码器（VAE），以学习分子结构的连续数值或潜在表示，从而扩展小分子识别的参考库。我们将 VAE 扩展为包括一个化学性质解码器，它被训练为一个多任务网络，以便根据所需的化学性质塑造潜在表示，使其按照所需的化学性质进行组装。该方法的独特之处在于它应用于代谢组学和小分子识别，其重点是可以从实验测量（/，CCS）中获得的性质，并与它的训练范例相结合，该范例涉及一系列转移学习迭代。首先，从具有/标签的大量结构数据集中学习分子表示。接下来，由于实验性质数据有限，将使用性质值继续训练。最后，通过使用实验数据进行训练，进一步改进网络。这允许网络在每个阶段尽可能多地学习，从而在没有过度拟合的情况下成功使用越来越小的数据集。一旦训练完成，该网络就可以直接从结构预测化学性质，也可以生成具有所需化学性质的候选结构。与 CCS 性质预测的第一性原理模拟相比，我们的方法快了几个数量级。此外，沿着由化学性质类似物定义的流形生成新分子的能力使 DarkChem 在许多应用领域非常有用，包括代谢组学和小分子识别、药物发现和设计、化学取证等。
