Freeform generative design of complex functional structures

Generative machine learning is poised to revolutionise a range of domains where rational design has long been the de facto approach: where design is practically a time consuming and frustrating process guided by heuristics and intuition. In this article we focus on the domain of flow chemistry, which is an ideal candidate for generative design approaches. We demonstrate a generative machine learning framework that optimises diverse, bespoke reactor elements for flow chemistry applications, combining evolutionary algorithms and a scalable fluid dynamics solver for in silico performance assessment. Experimental verification confirms the discovery of never-before-seen bespoke mixers whose performance exceeds the state of the art by 45%. These findings highlight the power of autonomous generative design to improve the operational performance of complex functional structures, with potential wide-ranging industrial applications.

Synthesis of HIV-1 capsid protein assembly inhibitor (CAP-1) and its analogues based on a biomass approach

A biomass-derived platform chemical was utilized to access a demanded pharmaceutical substance with anti-HIV activity (HIV, human immunodeficiency virus) and a variety of structural analogues. Step economy in the synthesis of the drug core (single stage from cellulose) is studied including flexible variability of four structural units. The first synthesis and X-ray structure of the inhibitor of HIV-1 capsid protein assembly (CAP-1) is described.