Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quality of life increases. The resistance of pathogens to multiple antibiotics is also increasing, adding an additional layer of complexity to the problems of employing safe and effective medical procedures. One approach to reducing the rate of infections associated with implanted and indwelling medical devices is the use of polymers that resist the formation of bacterial biofilms. To significantly accelerate the discovery of such materials, we show how state of the art machine learning methods can generate quantitative predictions for the attachment of multiple pathogens to a large library of polymers in a single model for the first time. Such models facilitate design of polymers with very low pathogen attachment across different bacterial species that will be candidate materials for implantable or indwelling medical devices such as urinary catheters, cochlear implants, and pacemakers.

Machine learning (ML) and artificial intelligence (AI) methods for modeling useful materials properties are now important technologies for rational design and optimization of bespoke functional materials. Although these methods make good predictions of the properties of new materials, current modeling methods use efficient but rather arcane (difficult‐to‐interpret) mathematical features (descriptors) to characterize materials. Data‐driven ML models are considerably more useful if more chemically interpretable descriptors are used to train them, as long as these models also accurately recapitulate the properties of materials in training and test sets used to generate and validate the models. Herein, how a particular type of molecular fragment descriptor, the signature descriptor, achieves these joint aims of accuracy and interpretability is described. Seven different types of materials properties are modeled, and the performance of models generated from signature descriptors is compared with those generated by widely used Dragon descriptors. The key descriptors in the model represent functionalities that make chemical sense. Mapping these fragments back on to exemplar materials provides a useful guide to chemists wishing to modify promising lead materials to improve their properties. This is one of the first applications of signature descriptors to the modeling of complex materials properties.

Human mesenchymal stem cells (hMSCs) are widely represented in ongoing regenerative medicine clinical trials due to their ease of autologous implantation. In bone regeneration, crosstalk between macrophages and hMSCs is critical with macrophages playing a key role in the recruitment and differentiation of hMSCs. However, engineered biomaterials able to both direct hMSC fate and modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-microtopography screening platform, the ChemoTopoChip, is used to identify materials suitable for bone regeneration by screening with human immortalized mesenchymal stem cells (hiMSCs) and human macrophages. The osteoinduction achieved in hiMSCs cultured on the "hit" materials in basal media is comparable to that seen when cells are cultured in osteogenic media, illustrating that these materials offer a materials-induced alternative in bone-regenerative applications. These also exhibit immunomodulatory effects, concurrently polarizing macrophages towards a pro-healing phenotype. Control of cell response is achieved when both chemistry and topography are recruited to instruct the required cell phenotype, combining synergistically. The large library of materials reveals that the relative roles of microtopography and material chemistry are similar, and machine learning identifies key material and topographical features for cell-instruction.

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