Prof. A. Marco Saitta
(IMPMC - Sorbonne Université, CNRS, MNHN, Paris, France)
 

Abstract:
In the study of the origins of life, the integration of ab initio calculations with machine learning is providing new insights. This research focuses on the formation of key biomolecules, such as glycine, a fundamental amino acid, and adenine, a nitrogenous base found in RNA and DNA. Using ab initio simulations and advanced sampling methods, we have identified a new pathway for glycine synthesis, the “oxyglycolate pathway,” which offers a simpler alternative to the conventional Strecker synthesis and may better account for the presence of glycine in meteorites. This result suggests that the molecular building blocks of life could have emerged through less complex chemical processes than previously assumed.
 
In parallel, our work on adenine synthesis has implications for astrobiology, particularly in connection with NASA’s upcoming Dragonfly mission to Titan. By combining ab initio simulations with machine learning techniques, we are investigating potential chemical pathways that may have led to adenine formation under prebiotic conditions, contributing to a better understanding of how essential biomolecules could have arisen on early Earth or in extraterrestrial environments such as Titan.
 
Machine learning methods play a central role in this research, facilitating the exploration of complex reaction networks and reducing computational costs, thereby enabling the identification of previously unrecognized pathways to fundamental biomolecules. These findings contribute to refining current theories on the chemical origins of life and offer a more efficient framework for future studies in prebiotic chemistry and astrobiology.
 

Zoom registration link:
https://zoom.us/meeting/register/zjqTmsTFS0i2xy-DcLLemQ