A machine learning approach for the discovery of new chemical receptors using a microfluidic platform

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Molecular receptors that can bind to specific analytes with high affinity and selectivity are of widespread interest and importance. For example, they have been used as chemical sensors, optical probes for cellular imaging as well as in the development of functionalized solid materials for the removal of pollutants. However, one of the biggest challenges in this area is the design of receptors, from scratch, with very high specificity and affinity for a given analyte. Often, this is either achieved serendipitously or not to the levels required for some applications. The main problem resides in the fact that the receptor-analyte binding event is highly sensitive to the geometry of the receptor, its size, charge and the chemical groups present in the structure. The optimization of these parameters often makes the synthesis of the receptors difficult or even not feasible. To overcome these problems, in this project we propose to combine a microfluidic platform for the high-throughput synthesis and testing of molecular receptors, with machine learning approaches to feed into the design of the new receptors. The project will initially focus on using the above-mentioned approaches to develop receptors to bind anionic species of environmental interest (e.g. arsenate, chromate and toxic derivatives of phosphates). However, we expect our combined microfluidics-machine learning approach to be generic for the development of molecular receptors for a wide range of different analytes.