We present an open-source chemoinformatic workflow to generate and analyze complex abiological chemical networks to discover novel compounds and autocatalytic processes. We demonstrate this pipeline's capabilities against a well-studied model system.

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.

The development of new technologies in recent years has highlighted interdisciplinarity as a tool to solve complex problems faced by scientists and engineers in research work. Worldwide, the area of space science, specifically astrobiology, has had more than 25 missions with high technological development and economic returns. However, the success of interdisciplinary teams requires collaboration, responsibility, and leadership on the part of all members to prioritize the main objectives of the research. Likewise, the formation of interdisciplinary teams can be affected because there is little information about the strategies and tools that recognize the opportunity for constant interaction between subjects from engineering and science. Using a known methodology based on other recent proposals, we include a description of the conformation and behavior of a research team and an analysis of interdisciplinarity through the interrelation and level of dependence of the existing subject categories in the “Killalab” team. In this research, we present the ensemble interdisciplinary group “Killalab” and its implications for the realization of astrobiological investigations.

This study investigates the stability of chiral-molecule solution phases, with a specific focus on amino acids. The model framework is based on a two-dimensional square lattice model, where individual sites may be occupied by oriented chiral molecules or structureless solvent particles. Utilizing the Glauber dynamics and statistical mechanical formalism, as previously introduced and examined by Lombardo et al., we explore the influence of temperature, amino acid concentration, enantiomeric excess, and homochiral interaction strength on nucleation mechanisms, equilibrium phase behavior, and crystal composition. Our findings offer thermodynamic insights into the chiral amplification process of amino acids, contributing to a deeper understanding of the underlying processes.