Chemical Biology is critical to supporting the development and translation of the next generation of molecular tools and technologies for making, measuring, modelling and manipulating molecular interactions in biological systems across multiple length scales. These include protein-nucleic acid, lipid-drug, lipid-nucleic acid and protein-protein interactions.

Despite their increasing importance as targets for intervention in biological systems, their exploitation is hampered by many factors which are being tackled by the ICB community. These include large contact surface areas, the absence of readily identifiable binding pockets, low concentrations of particular binding partners, the multi-component nature of these interactions, the multiple time and length-scales over which they occur and a limited understanding of how these interactions combine to generate higher order functions.

The development of novel molecular tools and technologies with enhanced quantitative capabilities that can address these bottlenecks is vital to providing the quantity, quality and type of data needed for predictive modelling and downstream commercial and medical exploitation of these spatially dispersed multipoint interactions. They also have the potential to generate IP with significant potential for commercial exploitation, in their own right (through commercialisation in the instrumentation sector). From enabling biological and biomedical research by unlocking the molecular mechanisms of disease, through to stimulating the design of improved agrochemicals and increasing our understanding of the link between diet and the microbiome, chemical biology is pivotal in reducing the spiraling cost of product development and healthcare.

In parallel, with the arrival of the 4th industrial revolution the life sciences are embarking on a transformative journey which is blurring the lines between disciplines and between man and machine. Developments in robotics are driving the integrated control of lab hardware enabling R&D workflow automation. Rapid prototyping is reducing technology innovation cycles. Big data offers unprecedented opportunities for AI and machine learning. AI is stimulating developments that can underpin smarter high-throughput approaches for data handling with the promise of offering creative insight. Coupled with the rise of machine learning it is now possible to promote knowledge driven systems design from discovery data.

Dovetailing these approaches with new molecular tools and technologies will lead to game-changing rapid design-test cycles that speed up product development.

Our scientific vision

The Scientific Vision of the ICB CDT encompasses four aims:

  1. Develop and validate novel tools and technologies for the study of molecular interactions and their applications to strategic biological/biomedical problems and industry priority areas that cannot be undertaken with the current state-of the art. This will connect physical/mathematical sciences innovation push with life-science pull. The expanded remit of the CDT now also embraces multi-scale modelling (e.g. graph theory) and experimental approaches (micro-physiological environments) for studying molecular interactions and biological systems.

  2. Dovetail these technologies with industry 4.0 approaches to maximise insights from molecular interaction studies.

  3. Translate research advances for exploitation in industrial/medical applications.

  4. Extend the capabilities of previous CDT-technologies, so that these can be translated to end users in different sectors.