Data Science & Artificial Intelligence:
Unlocking new science insights
Data science and AI has the potential to transform the way we discover and develop new medicines – turning yesterday’s science fiction into today’s reality with the aim of enabling the translation of innovative science into life-changing medicines
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We are using AI to help us analyse and interpret huge quantities of data at all stages of drug discovery and development with the aim of:
- Gaining a better understanding of the diseases we want to treat
- Identifying new targets for novel medicines
- Driving personalised medicine strategies
- Speeding up the way we design, develop and make new drugs
Building disease understanding through knowledge graphs
If you’ve ever asked Google or Alexa a question, you will have tapped into knowledge graphs.
These bring information together from thousands of different sources to find you the answer you need.
At AstraZeneca, we’re using knowledge graphs to give our scientists the information they need about genes, proteins, diseases and compounds and how they relate to each other. Using AI to combine information from multiple sources we hope to draw more accurate conclusions than if we analysed science literature by hand. AI also has the potential to find patterns in these graphs revealing previously unexplored hypotheses.
Our knowledge graphs integrate genomic, disease, drug and safety information, so our researchers can ask key questions to help identify and prioritise drug targets. As our data and knowledge evolves so will our graphs, so every new experiment can benefit from everything learned before.
Ultimately, we want to develop personalised knowledge graphs that bring the right information to the right scientist, at the right time.
Revolutionising drug discovery with augmented drug design
Through AI, we have the potential to transform medicinal chemistry, augmenting traditional design with sophisticated computational methods to predict what molecules to make next and how to make them.
We are using AI to help us discover new medicines.
It is reducing the time it takes to discover and refine a potential drug candidate by two thirds, from three years to just one year.
AI is enabling us to search about 1060 molecules for promising leads. Previously, it hasn't been possible – now, we can do it in minutes.
We are training AI-based tools to modify existing molecular structures to create new ones, and score them to see if they could be useful in our research projects.
Having identified promising molecules, the next step is to optimise the way they are made. AI is starting to help here too – enabling us to plan the multiple steps that are needed to synthesise new chemicals.
We want to use AI in drug discovery to build the chemistry lab of tomorrow – not only for discovering and making new drugs but for automating and speeding up the repeated cycles of generating, validating and testing high-quality compounds.
Read more about our approach to using AI in drug discovery
Using AI for fast, accurate image analysis
Every week, our pathologists analyse hundreds of tissue samples from our research studies. They check them for disease and for biomarkers that may indicate patients most likely to respond to our medicines. It is very time consuming which is why we are training AI systems to assist pathologists in analysing samples accurately and more effortlessly. This has the potential to cut analysis time by over 30%.
For one of our AI systems, we implemented an approach inspired from how some self-driving cars understand their environment. We trained the AI system to score tumour cells and immune cells for a biomarker, called PD-L1, which has potential to help inform immunotherapy-based treatment decisions for bladder cancer.
Our AI system looks at thousands of images from tissue samples, methodically checking each one for PD-L1. It saves our pathologists time and is especially useful in difficult cases.
Building the right data backbone
Today we are generating and have access to more data than ever before. Data and analytics have the potential to transform our business, but the true value of scientific data can only be realised if it is “FAIR”, or Findable, Accessible, Interoperable and Reusable.
AstraZeneca’s R&D and IT groups are partnering closely to create an industry-leading enterprise data and AI architecture. This will help us answer key business questions and enhance our ability to leverage tools, such as AI and machine learning, both now and in the future.
To this end, we are mobilising a team of data scientists, bioinformaticians, data engineers and machine learning experts from across the company to ensure we are collecting, organising and using the right data, in the best way.
Across AstraZeneca, we are transforming our approach to data. We are renewing our IT architecture and embedding advanced analytics tools. We’re ensuring they are fully flexible and scalable and that our data and AI capabilities match the scope of our ambition – to accelerate the discovery, development and delivery of life-changing medicines.