AI for drug discovery & clinical research

AI is taking centre stage in the conversation around biomedical research. The importance of AI for drug discovery & clinical research can’t be overstated. It’s one of the most popular ways to cut costs and reduce time while minimising failure rates. Today, we’ll dive more into the ways that it’s being used.

AI in drug discovery

The first place where artificial intelligence supports is with target identification & validation. You see it in genomics and proteomics data analysis, combined with machine learning for druggable target identification. In novel drug discovery, AI can accurately extract important features and generate valid prediction models to generate strong leads on potential new drugs. As such, AI models are proving to be invaluable for novel compound design, QSAR modelling and prediction of bioactivity. They allow for multi-objective optimisation including efficacy, safety and ADMET. It’s also a big part of drug candidate screening. Here, it can apply deep learning to make better selections. AI is great at knowledge graphs and data mining of literature, so teams can also use it to identify new indications for old drugs. This is one of the lesser-discussed applications, but further adoption would allow pharmaceutical companies to find additional monetisation routes for their current portfolio. It’s also a win societally, as finding new treatments and cures within our existing drugs means a simple manufacturing ramp-up can solve more medical dilemmas for people.

AI in preclinical & clinical research

In preclinical modelling, AI is useful for things like silico simulations, toxicity prediction, animal model analysis and pathology. Within clinical trial design, patient stratification and site selection are faster with AI support. Patient recruitment and retention can largely be automated, using natural language processing for EHR mining, while generative AI is used for outreach. Predictive analytics can calculate enrollment rates and dropout risks, accounting for them with over-enrollment tactics. While on the trials, IoT enables real-time monitoring via wearables, and AI logic can also be applied to remote patient monitoring to reduce risks. Finally, when it comes to post-trial analysis, AI again takes centre-stage to make sense of all the data and suggest conclusions (of course, always reviewed by the human clinical team).

Supporting technologies and the future of AI in pharma

AI doesn’t act alone, however. It’s supported by supervised, unsupervised and reinforcement machine learning alongside natural language processing, computer vision for pathology and imaging and knowledge graphs or data integration platforms for human oversight. All this needs to exist within an appropriate regulatory framework for data privacy and HIPAA/GDPR compliance. Finally, the tools employed need to be ethically designed, accounting for bias in algorithms and datasets with full transparency and explainability (XAI). As we move forward with the integration of multi-omics and precision medicine, federated learning and decentralised data collaboration; we’re essentially greenlighting human-AI co-creation in research. So, we must be introspective and critical of the evolving role of AI in clinical and research pathways. 

Sure, AI has transformative potential, but only when used responsibly. 

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