Making a drug or pesticide often requires sticking a chlorine or bromine atom onto a molecule, but current industrial methods rely on toxic chlorine and bromine gas, produce messy mixtures, and can only work on simple starting materials. This project will hunt for natural enzymes—halogenases—that can do the job cleanly, using ordinary salt as the halogen source and placing the atom precisely onto complex drug-like molecules, even at a late stage of synthesis. The team will combine bioinformatics, lab screening, and AI to discover new halogenases, then use AI-guided evolution to improve them, and develop ways to recycle the enzymes’ cofactors so the process can be scaled up. If successful, this would replace fragile supply chains for elemental halogens—much of which come from energy-intensive plants in India, China, Russia, and Ukraine—with a cheaper, greener, and more selective biocatalytic route. For pharmaceutical and agrochemical companies, that means being able to halogenate complex drug candidates directly, rather than designing the whole molecule around a simple halogenated building block, accelerating drug discovery and reducing chemical waste.
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This Open Fellowship Plus application focusses on discovery, development and innovation enabling precision molecule editing and diversification, an area central to drug discovery and of great interest to our pharmaceutical industry partners. It also looks to examine and address diversity across the science + engineering community involved in translation, with a particular focus on the largest population grouping (women) who remain significantly under-represented in spinouts and start-ups. The formation of C-X bonds (where X is F, Cl, Br, or I) is of great importance to the pharmaceutical and agrochemical industries. The introduction of a halogen into a molecule can be used to modulate bioactivity, bioavailability and metabolic stability. It also provides a chemically reactive and selectively functionalisable handle, that can be used to build or diversify molecules. For these reasons >81% of agrochemicals contain a C-X bond, and for pharmaceuticals >26% contain a C-Cl bond with a further 67% requiring a C-Cl bond for assembly. Current industrial approaches to making C-X bonds still require Cl2 and Br2. Such approaches rely on fragile supply chains with much of the elemental halides being generated through energy expensive processes in India, China, Russia, Ukraine, and require the C-X bond to be introduced at an early stage. Most critically, these approaches lack selectivity and, even when applied to simple starting materials, result in hard to separate mixtures. For this reason, only simple halogenated building blocks are generated. To incorporate a halogen into a molecule, whether that be a pharmaceutical or agrochemical, its assembly must be designed using these simple halogenated building blocks. Transitioning from current thinking to new thinking + discovery In contrast to current industrial approaches to halogenation, enzymes confer exquisite selectivity, enabling precision late-stage halogenation. Unlike current industrial approaches, salt is used as halogenating agent, only one product is generated simplifying purification, and complex bioactive scaffolds, rather than simple building blocks, can be accepted as substrates. In this ambitious fellowship proposal, we will: - use bioinformatics approaches, coupled to wet screening and AI to discover new halogenases - develop and apply AI guided directed evolution and selection to these new halogenases - explore innovative new approaches to cofactor recycling toward enabling reaction intensification and scale up - demonstrate precision late-stage diversification of pharmaceutically relevant scaffolds, developing new and innovative diversification procedures. Demonstrating PRIMED for Diversification in the context of pharmaceutical design and discovery. The proposed work is poised to bring significant advantage and acceleration to molecule making and diversification, particularly in the context of drug discovery. It will also bring benefit to biocatalysts through the development and pioneering of AI informed enzyme selection. Further insight and benefit will be brought through the Open Plus component, shining a light on diversity data within the translational arena.
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