论文标题
深度分子编程：二元重量恢复神经网络的自然实施
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
论文作者
论文摘要
与传统电子不符的分子环境中的嵌入计算预计将对合成生物学，医学，纳米制作和其他领域产生广泛的影响。剩下的一个关键挑战在于为分子计算开发编程范例，该范例与基础化学硬件齐全，并且不尝试制造不合适的电子范式。我们发现了流行的一类神经网络（二进制重量relu aka binaryconnect）与一类对反应速率绝对可靠的耦合化学反应之间的紧密联系。速率无关化学计算的鲁棒性使其成为生物工程实施的有希望的目标。我们展示了如何使用结合的深度学习优化技术在计算机中训练的二进制连接神经网络，可以将其编译到等效的化学反应网络，从而提供一种新型的分子编程范式。我们在范式虹膜和MNIST数据集上说明了这种翻译。为了预期的化学计算应用，我们进一步使用我们的方法来产生一个可以根据基因表达水平区分不同病毒类型的化学反应网络。我们的工作为神经网络与分子编程社区之间的丰富知识转移奠定了基础。
Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and other fields. A key remaining challenge lies in developing programming paradigms for molecular computation that are well-aligned with the underlying chemical hardware and do not attempt to shoehorn ill-fitting electronics paradigms. We discover a surprisingly tight connection between a popular class of neural networks (binary-weight ReLU aka BinaryConnect) and a class of coupled chemical reactions that are absolutely robust to reaction rates. The robustness of rate-independent chemical computation makes it a promising target for bioengineering implementation. We show how a BinaryConnect neural network trained in silico using well-founded deep learning optimization techniques, can be compiled to an equivalent chemical reaction network, providing a novel molecular programming paradigm. We illustrate such translation on the paradigmatic IRIS and MNIST datasets. Toward intended applications of chemical computation, we further use our method to generate a chemical reaction network that can discriminate between different virus types based on gene expression levels. Our work sets the stage for rich knowledge transfer between neural network and molecular programming communities.
