Blog

Interpret - Constructing large scale biomedical knowledge bases from scratch

Drug discovery is a complex process. For a scientist, the goal is to identify targets (genes) that are involved in the development of a disease.

The discovery of targets requires a great deal of exploration of existing biomedical literature (millions of papers, patents, clinical trials and databases), and inference of new relationships between biomedical entities. This is an ambitious task for a scientist alone. To address this problem, we have developed machine learning methods to improve the way scientists make sense of huge amounts of data and discover non-obvious relationships between genes, diseases and molecules, based on existing relationships.

Extracting facts from the biomedical literature that are usable for real-life applications, such as drug discovery, is a very challenging task. If done manually, it is simply not possible for scientists alone to read and process the whole corpus of biomedical information.  If we use unsupervised methods (e.g. OpenIE), the relevance of the extracted relations will not be guaranteed, because emerging patterns do not necessarily align with the scientist’s requirement. Finally, if we use a supervised method, we need labeled data (e.g. a list of curated entity pairs or positive/negative examples of sentences expressing relationships) which are often scarce or not appropriate for the given use case.

To address these issues, we have created a new method, interpret. The system extracts existing facts without requiring any training data or hand-crafted rules. Instead, it discovers and recommends patterns rather than prescribing them.

We start by extracting interpretable patterns from sentences (see workflow diagram on the right). This is a totally automated process which is done solely using unstructured data from the biomedical corpus. We discover patterns such as “role of GENE in DISEASE”, “GENE target for DISEASE” or “DISEASE caused by GENE mutation”. We then ask our drug discovery scientists to validate which patterns best describe the relationships of interest. Within minutes, they are able to identify patterns that can be used to retrieve  thousands of new facts. As an example, the following sentence was surfaced as supporting the top pattern and provides a clear therapeutic relationship between gene “IL10” and disease “lupus nephritis”: “This proves the importance of IL10 in the pathogenesis of lupus nephritis.”

The interpret method is, by design, easily transferable to other types of biomedical entities.  Types of relationships can be extended to complex “n-ary” relationships involving multiple entities e.g. "knockdown of GENE affects DISEASE in TISSUE".

Interpret allows drug discovery scientists to generate relevant new pairs for application very rapidly. We also show that the addition of these new pairs can improve the precision of a downstream knowledge base completion task which is used to infer novel biomedical facts beyond what is reported in the literature and improve the likelihood of discovering novel targets.

More Posts

You Might Also Like

Blog
Transforming drug discovery with AI: how we’re building and nurturing the best talent for the job
At BenevolentAI, we are on a mission to bring life-changing medicines to patients, and we are looking for collaborative, mission-driven people to join our tech, drug discovery and business operations teams in London, Cambridge and New York.
Oct 17, 2021
News
BenevolentAI identifies novel target for ulcerative colitis and advances candidate to IND/CTA-enabling studies
BenevolentAI’s AI-Drug Discovery platform uncovered a novel target not previously linked to ulcerative colitis and advanced candidate to preclinical studies.
Oct 14, 2021
Blog
Measuring bias: moving towards more inclusive health research outcomes #stateofai
Having shared our open-source Diversity Analysis Tool last year, we were tasked to investigate and demonstrate the lack of diversity in biomedical data as part of the State of AI Report 2021.
Oct 12, 2021
Blog
Expert-augmented computational drug discovery for rare diseases
Combining scientific expertise, computational tools and our AI-enhanced biomedical knowledge graph to successfully uncover a new drug combination for treating a rare brain cancer in children.
Sep 28, 2021
News
BenevolentAI appoints Dr John Orloff to its Board of Directors
Biopharmaceutical veteran Dr John Orloff joins the BenevolentAI Board as a Non-Executive Director as it scales the development of its leading AI-derived drug pipeline.
Sep 9, 2021
Blog
AI in Drug Discovery
This blog seeks to demystify the application of artificial intelligence (AI) and machine learning (ML) in drug discovery by exploring some of the challenges, opportunities and progress that has been achieved in the field so far.
Jul 13, 2021