Research and development in drug discovery will need to find significant efficiency gains if the industry is to continue generating novel drugs. There is great expectation for machine learning (ML) to provide this boost in R&D productivity, but to harness the full potential of ML, the generation of new, high-quality datasets will be necessary. Here, the authors present a platform that combines high-throughput display and selection data generation with ML. More specifically, deep learning is used to inform the directed evolution of novel biotherapeutics using DNA library synthesis, ultra-high throughput selections, and next generation sequencing. By combining the learnings of multiple in silico models, their platform enables multi-parameter optimisation across multiple important protein characteristics. They also present a model for benchmarking these ML-driven drug discovery platforms according to the accuracy of their underlying in silico models, in conjunction with the throughput of their empirical experimentation.

Plant-derived natural compounds have been used for treating diseases since prehistorical times. The supply of many plant-derived natural compounds for medicinal purposes, such as thebaine, morphine, and codeine, is primarily dependent on opium poppy crop harvesting. This dependency adds an extra risk factor to ensuring the supply chain because crops are highly susceptible to environmental conditions. Emerging technologies, such as biocatalysis, might help to solve this problem by diversifying the sources of supply of these compounds. Here we review the first committed step in the production of alkaloid painkillers, the production of S-norcoclaurine, and the enzymes involved. The improvement of these enzymes can be carried out experimentally by directed evolution and rational design strategies, supported by computational methods, to create variants that produce the S-norcoclaurine precursor for alkaloid painkillers in heterologous organisms, meeting the pharmaceutical industry standards and needs without depending on opium poppy crops.

Directed evolution is an incredibly powerful strategy for engineering enzyme function. Applying this approach to glycosidases offers enormous potential for the development of highly specialized tools in chemical glycobiology. Performing enzyme directed evolution requires the generation, by random mutagenesis, of mutant libraries from which large numbers of variant enzymes must be screened in high-throughput assays. A structure-guided \"semirational\" method for library creation allows researchers to target specific amino acid positions for mutagenesis, concentrating mutations where they might be most effective in order to produce mutant libraries of a manageable size, minimizing screening effort while maximizing the chances of finding improved mutants. Well-designed assays, which may use specially prepared substrates, enable efficient screening of these mutant libraries. This chapter will detail general methods in the structure-guided directed evolution of glycosidases, which have previously been employed in engineering a blood group antigen-cleaving enzyme.

Throughout the history of evolutionary theory a number of scientists have argued that evolution proceeds along a limited number of definite trajectories, a concept and group of theories known as \"orthogenesis\". Beginning in the 1880s, influential evolutionists including Theodor Eimer, Edward Drinker Cope, and Leo Berg argued that a fully causal explanation of evolution must take into account the origin and nature of variation, an idea that implied orthogenesis in their views. This paper argues that these orthogenesis developed theories that were more than highly technical and theoretically dubious hypotheses accessible only to elite specialists, as certain histories of these ideas might suggest. Some orthogenesists made their case to a non-specialist audience to gain support for their ideas in the face of widespread controversy over evolutionary theory. Through a case study analysis of three major books by Eimer, Cope, and Berg, this paper contends that they sought to re-orient the central tenets of the science of evolution to include the causal impact of variation on evolutionary outcomes. These orthogenesists developed novel and synthetic evolutionary theories in a publishing platform suited for non-specialist audiences in an effort to impact the debates over evolutionary causation prevalent in the late-19th and early 20th centuries.