The use of artificial intelligence (AI) is growing, from banking and biodiversity to travel and transport. One of the areas where its presence is significant is healthcare, and its existing and potential applications include: [1-6]
AI clinicians
Augmented telehealth
Disease detection, diagnosis and assessment
Imaging analysis in diagnostics
Wound assessment
Disease prediction
Promoting mental health and weight loss via apps
Virtual wards
Digital biomarkers
Measuring movement and behaviour
Decision-making support
One of the key areas where AI is playing a significant role is drug discovery and development. Currently, drug discovery can involve trial-and-error experimentation to select compounds, followed by large-scale testing. This hit and miss process can be costly and lengthy – up to 15 to 20 years of research – and may have low levels of accuracy and high failure rates. It also doesn’t always predict the efficacy and safety that the candidate compounds will exhibit in clinical trials. By analysing large quantities of data from benchtop research, preclinical trials and patient records, AI has potential to make major improvements to this route to the clinic through the identification of new targets and prediction of efficacy, safety and toxicity. [7, 8]
A (very) brief introduction to AI
In AI, computers and machines can simulate human comprehension, learning, creativity and autonomy, and mimic problem solving and decision making. AI is built on machine learning (ML – systems that learn from historical data) and deep learning (machine learning models that mimic human brain function). Generative AI (GenAI) builds further on machine learning and deep learning to create original text, images, video and other content. [9]
Identifying drug targets
Machine learning algorithms can be used to analyse large datasets and identify the targets that are mostly likely to interact with a potential drug. The datasets used include gene expression profiles, protein-protein interaction networks, biological pathways, clinical and chemical databases and unstructured data such as scientific literature. The targets can then be prioritised using ML algorithms such as support vector machines (SVMs) and neural networks. [6, 10]
ChatGPT (a natural language processing system) and large language models (LLM) are also being used to identify new drug targets. [8]
Finding better drugs
Ideal drugs will have on-target effectiveness with few or no off-target effects. Deep learning trained with known compounds and properties can be used to find existing molecules or design new molecules that have the desired activity, solubility and safety. Virtual screening, which can be structure-based or ligand-based, can speed the selection of candidate drugs. [3, 6, 11]
The combination of deep learning and interpretable machine learning can support de novo drug design and molecular dynamics in drug discovery. The parameters used include molecular similarity, quantitative structure–activity relationship (QSAR) and the process of generation of molecules. [3, 11]
New drugs can also be discovered using AI-powered imaging technology. Microscopy images from human cell assays, taken when cells have genes knocked out or different compounds added, can be compared using trained AI models that indicate biological differences between samples, thus identifying the compounds that could be the most effective. [10]
Predicting efficacy and safety
There are challenges with using animal studies and clinical trials to determine efficacy, safety and toxicity, as animal studies do not always fully predict either safety or efficacy and clinical trials are expensive and lengthy, and may put people at ris...
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