Brown University, Providence, RI, USA.
Nat Commun. 2020 Feb 4;11(1):691. doi: 10.1038/s41467-020-14455-1.
Multicomponent reactions enable the synthesis of large molecular libraries from relatively few inputs. This scalability has led to the broad adoption of these reactions by the pharmaceutical industry. Here, we employ the four-component Ugi reaction to demonstrate that multicomponent reactions can provide a basis for large-scale molecular data storage. Using this combinatorial chemistry we encode more than 1.8 million bits of art historical images, including a Cubist drawing by Picasso. Digital data is written using robotically synthesized libraries of Ugi products, and the files are read back using mass spectrometry. We combine sparse mixture mapping with supervised learning to achieve bit error rates as low as 0.11% for single reads, without library purification. In addition to improved scaling of non-biological molecular data storage, these demonstrations offer an information-centric perspective on the high-throughput synthesis and screening of small-molecule libraries.
多组分反应能够从相对较少的反应物中合成大型分子文库。这种可扩展性使得制药行业广泛采用这些反应。在这里，我们采用四组分 Ugi 反应来证明多组分反应可以为大规模分子数据存储提供基础。通过这种组合化学，我们对超过 180 万比特的艺术历史图像进行了编码，其中包括毕加索的一幅立体派画作。使用机器人合成的 Ugi 产物文库写入数字数据，并使用质谱法读取文件。我们结合稀疏混合物图谱和有监督学习，实现了单读的误码率低至 0.11%，而无需文库纯化。除了改进非生物分子数据存储的扩展能力外，这些演示还提供了一种以信息为中心的观点，用于小分子文库的高通量合成和筛选。
