Making the chemical building blocks of life in a way that is “prebiotically plausible” — that is, likely to have occurred naturally on Earth — is a central pillar of research in this area.³ In the 21st century there has been considerable success in obtaining multiple biochemicals, relevant to different aspects of the living organism, from the same feedstock and environment.⁴ Experiments have demonstrated that a small number of starter chemicals can lead, with minimal intervention and often with self-organising, highly robust reaction networks, to hundreds of products.⁵

Future Horizons:

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5-yearhorizon

Automation begins to pay off

Chemical systems have been developed that display open-ended evolution: that is, avoiding equilibrium. Increased use of automation and AI allows us to conduct high-throughput experiments.

10-yearhorizon

Chemical computation becomes possible

“Protocells” with self-replicating nucleic acid driven by metabolic reactions are created. Laboratory experiments utilising a small array of reactive compounds have the topology and kinetics necessary to carry out basic computational processes via chemical reactions. Network-level descriptions of both living and non-living chemical systems are used to distil a small number of correlative factors associated with the expression of “life-like attributes” in those systems.

25-yearhorizon

Predictions of life-like chemistry becomes possible

We have systematic comparisons of the prebiotic chemical potential of different geological settings. Naturally occurring reactive compounds are shown to have the necessary topology and kinetics to permit emergent information-processing systems to form as predecessors to living systems. Systems-level descriptions of living entities are sufficiently sophisticated to permit direct predictions of the frequency of occurrence of chemical systems with life-like behaviours, which can in turn be used to infer the probability of life arising spontaneously under generic prescribed conditions.

Commensurately, researchers are studying how individual chemical reactions can aggregate to form network-level chemical systems that demonstrate attributes that we generally associated with life.⁶,⁷,⁸ Machine learning may prove to be a useful tool for understanding the properties of these chemical networks.⁹

There is a growing scientific understanding of how systems of chemicals can change over time, and in particular what it might mean for them to “evolve” in the absence of true genetic control. Recent findings indicate multiple characteristics that could be used to define a genuinely “complex” chemical predecessor to life at the systems level.¹⁰,¹¹ These might include: an emergent set of chemicals or processes that is robust even amid changes to the rest of the system; systems that are far from chemical and thermodynamic equilibrium (a non-equilibrium state is one of the central features of life); or emergent chemical systems that are capable of processing information (but which do not require explicit structures, such as genes or the ribosome, to store or process biological genetic information).¹²

Prebiotic chemistry - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

The uncertainty related to future science breakthroughs in the field
The transformative effect anticipated breakthroughs may have on research and society
The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.