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Prebiotic chemistry has dominated the origins of life (OoL) field to such an extent that often these terms have been used interchangeably. While prebiotic chemistry has benefited from cross-seeding with other disciplines (e.g., biochemistry, biophysics, computational sciences, geosciences), the OoL problem is still primarily a chemical one. The range of possible non-enzymatic pathways provisioning important biomolecules is becoming clearer, and the problem can now be cast in terms of understanding how the constructive interplay between such pathways is underlying the mechanisms controlling their self-organization into systems. Those systems must be capable of producing their constituent parts and reproducing as a result, with a degree of maintaining and transferring their information content. This chapter provides a concise historical overview of the research field, presents a comprehensive panorama of the possible synthesis pathways for each biomolecular domain, discusses the homochirality conundrum, argues a case for experimentation using non-classical materials, and models, and finally considers current and future trends.

Background: The Darwinian concept of ‘survival of the fittest' has inspired the development of evolutionary optimization methods to find molecules with desired properties in iterative feedback cycles of synthesis and testing. These methods have recently been applied to the computer-guided heuristic selection of molecules that bind with high affinity to a given biological target. We describe the optimization behavior and performance of genetic algorithms (GAs) that select molecules from a combinatorial library of potential thrombin inhibitors in ‘artificial molecular evolution' experiments, on the basis of biological screening results. Results: A full combinatorial library of 15,360 members structurally biased towards the serine protease thrombin was synthesized, and all were tested for their ability to inhibit the protease activity of thrombin. Using the resulting large structure–activity landscape, we simulated the evolutionary selection of potent thrombin inhibitors from this library using GAs. Optimal parameter sets were found (encoding strategy, population size, mutation and cross-over rate) for this artificial molecular evolution. Conclusions: A GA-based evolutionary selection is a valuable combinatorial optimization strategy to discover compounds with desired properties without needing to synthesize and test all possible combinations (i.e. all molecules). GAs are especially powerful when dealing with very large combinatorial libraries for which synthesis and screening of all members is not possible and/or when only a small number of compounds compared with the library size can be synthesized or tested. The optimization gradient or ‘learning' per individual increases when using smaller population sizes and decreases for higher mutation rates.