Despite its correct predictions, Grzybowski’s reaction network is still a little theoretical. We also need to know how fast each of the reactions goes, and whether the by-products from earlier reactions will interfere with later ones. Huck, the chemist at Radboud University in the Netherlands, and his colleagues have begun tackling this with help from machine learning.
In a study published in 2022, Huck’s team performed the formose reaction, which creates sugars from simple carbon-based molecules. Given that a sugar called deoxyribose is used to make DNA, making sugars is a crucial early step in the origins of life. The formose reaction does this, but there’s a catch. It tends to undergo a “combinatorial explosion,” says Huck: it produces dozens or hundreds of products, which vary enormously depending on the exact conditions.
Huck’s team carried out the reaction in small flow chambers to keep it under control. They varied a number of conditions, including the temperature and the availability of different chemicals; stopped it once it had made a few dozen chemicals; and analyzed the mixture.
Environmental conditions like temperature determine what products are formed in the reaction, says Huck. However, it’s not obvious how or why: tiny changes in conditions sometimes have little effect, but sometimes they lead to drastically different outcomes. That’s where machine learning came in: after some training, the software was able to predict what the reaction would spit out. This takes us a step closer to understanding the conditions for making sugars on the primordial Earth.
Determining the environmental conditions and other parameters that prevailed at the time is one of the biggest problems for origins-of-life research, says Wentao Ma, a computer modeler at Wuhan University in China. Techniques like machine learning will help narrow it down. In a 2021 study, Ma and his colleagues simulated a mixture of nucleic acids. Using machine learning, they were able to find the optimal conditions for creating nucleic acids that could speed up the formation of their own building blocks—the kind of virtuous circle on which life depends.
Finally, machine learning can also help create high-fidelity simulations of the precise mechanisms by which chemical reactions happen—which is crucial for predicting when they will and won’t work. Key tools for this are computer models that simulate all the atoms in a mixture as they bounce around and interact with each other. “When performing the simulation, we can have access to the microscopic behavior of the system,” says Timothée Devergne, a modeler at Sorbonne University in Paris.