Sensory-driven microinterventions for improved health and wellbeing

The five senses are gateways to our wellbeing and their decline is considered a significant public health challenge which is linked to multiple conditions that contribute significantly to morbidity and mortality. Modern technology, with its ubiquitous nature and fast data processing has the ability to leverage the power of the senses to transform our approach to day to day healthcare, with positive effects on our quality of life. Here, we introduce the idea of sensory-driven microinterventions for preventative, personalised healthcare. Microinterventions are targeted, timely, minimally invasive strategies that seamlessly integrate into our daily life. This idea harnesses human's sensory capabilities, leverages technological advances in sensory stimulation and real-time processing ability for sensing the senses. The collection of sensory data from our continuous interaction with technology - for example the tone of voice, gait movement, smart home behaviour - opens up a shift towards personalised technology-enabled, sensory-focused healthcare interventions, coupled with the potential of early detection and timely treatment of sensory deficits that can signal critical health insights, especially for neurodegenerative diseases such as Parkinson's disease.

VECT-GAN: A variationally encoded generative model for overcoming data scarcity in pharmaceutical science

Data scarcity in pharmaceutical research has led to reliance on labour-intensive trial and error approaches for development rather than data driven methods. While Machine Learning offers a solution, existing datasets are often small and noisy, limiting their utility. To address this, we developed a Variationally Encoded Conditional Tabular Generative Adversarial Network (VECT GAN), a novel generative model specifically designed for augmenting small, noisy datasets. We introduce a pipeline where data is augmented before regression model development and demonstrate that this consistently and significantly improves performance over other state of the art tabular generative models. We apply this pipeline across six pharmaceutical datasets, and highlight its real-world applicability by developing novel polymers with medically desirable mucoadhesive properties, which we made and experimentally characterised. Additionally, we pre-train the model on the ChEMBL database of drug-like molecules, leveraging knowledge distillation to enhance its generalisability, making it readily available for use on pharmaceutical datasets containing small molecules, which is an extremely common pharmaceutical task. We demonstrate the power of synthetic data for regularising small tabular datasets, highlighting its potential to become standard practice in pharmaceutical model development, and make our method, including VECT GAN pretrained on ChEMBL available as a pip package.