Through the combined capabilities of our AI Platform, scientific expertise and wet lab facilities, we are well positioned to deliver novel drug candidates with a higher probability of clinical success than those developed using traditional methods. 

  • Too many patients are suffering from untreated or poorly treated diseases. We put patients first and use our disease-agnostic AI drug discovery Platform to expand the search for new treatments and increase the likelihood of success in diseases that have defied conventional research efforts.
  • Many medicines simply don't work in the patients for which they are prescribed. By embedding a precision medicine-based approach from the outset, we are able to more accurately identify patient subgroups most likely to respond to treatment.
  • We build our technology and tools to augment and enhance human ingenuity, in order to empower our scientists to better understand the development of disease mechanisms and find treatments for the patients who need them. We do this by keeping humans in the loop through human-centric design. Our technology is carefully developed and used with human oversight and a feedback system to make sure AI brings real and meaningful value to our research.
  • We rigorously query, test and validate the outputs of our AI predictions. The quality and validity of our AI-derived hypotheses are tested in assays and via world-class experimental protocols.
  • Each delivery area has a defined hierarchy of metrics that measure the relationship between our AI outputs and success in biological testing intended as progress towards portfolio entry. These metrics are closely monitored, their lead–lag relationship is continuously assessed and their trajectory helps support data-driven decision making within delivery areas, thereby shaping our AI. Examples of these metrics include the relationship between model performance on a curated benchmark and success in the screening cascade used to validate targets within a given deployment.
  • Beyond individual metrics, we conduct “end-to-end” analyses of our deployments to better understand the interplay of AI-driven hypothesis generation with testing in assay.

Examples of our evaluation work include:

"End-to-end" deployment analyses in TargetID that test the relationship between assay results and our Platform and how this can augment decision making in the TargetID pipeline.

Evaluation of PMPT models on an endotype-specific preclinical benchmark as a ground-truth measure of endotype identification.

In-depth analyses assessing the number of unique edges identified by our NER+RE models that come exclusively from unstructured data.

  • We collaborate with a range of stakeholders to promote thoughtful leadership in AI and are committed to the responsible use of AI in drug discovery, drawing on scientifically rigorous and multidisciplinary approaches and calibrating our work with the community’s feedback (BIA · TECH UK).
  • We actively explore and apply innovative ways to understand the reasons why the AI has made the suggestions that it has and where it could be further improved in order to verify results more efficiently and identify additional areas of improvement.
  • Our scientists have strong requirements to understand the biological rationale –  instead of our AI being a "black box", they are able to query how results were produced by the AI and check results through the latest lab-based technology and experimental techniques.
  • Strong governance in our industry protects patients and avoids risks in deploying AI.
  • We seek to avoid and mitigate any unjust impacts of bias in data sets on the development of treatments. 
  • We actively strive to address the lack of diversity in biomedical data. Our Data Diversity Initiative exists to propose tangible solutions to this urgent issue, raise awareness and act as a forum to drive change.
  • We uphold high standards of data integrity and privacy, only using data from reputable sources or data that we have generated ourselves. We respect the privacy and rights of data subjects where personal data is involved.
  • Where we use patient data in our work, it is pseudonymised at the point of ingestion or anonymised at source before we ingest it. We have clear patient level data management in place and we preserve patient privacy by restricting the use of personal data to only attributes that are necessary and likely to help us improve patient outcomes.