- AI for Drug Discovery – Creating more useful hypotheses and tests to discover new compounds and make better use of existing compounds.
- AI for Diabetic Retinopathy – Diagnosing a specific and subtle eye disease with computer vision.
Challenge
Recommendation systems are oft-used in the business world to match consumers with products. The reason for this is that each person is different concerning personal tastes. Similarly, people’s genetic makeup is different – and this is where a recommendation model may be helpful. In this particular case, to “match” individuals with potentially compatible pharmaceuticals. The stakes are high, as the process of bringing a drug to market is both time and resource-intensive.On average, a new drug takes an average of seven years and $3 billion to develop, from idea to prescription. Many pitfalls await nascent drug ideas. Priorities shift. The drug development process moves too slowly. In the end, two of every three products do not survive to become drugs.Actions Taken
“There’s a tremendous amount of data and knowledge locked inside an organization,” explains Bill Fox, SambaNova’s Healthcare and Life Sciences Lead. “It’s siloed, and in people’s heads, notebooks, or in a database they created. It’s in different formats. Some are structured. Some aren’t. That’s difficult to pull together.” It is also apparently difficult to pull together the tremendous amount of data in medical literature, the most voluminous and ubiquitous of which being peer-reviewed journals. Researchers across the various medical disciplines, including pharmaceuticals, use these data to conduct research and construct medical articles of their own. Perhaps one of the most promising use cases of machine learning is to help researchers with this arduous task. Elaborating on the use of natural language learning in this regard, Amir Saffari, Senior Vice President of AI at BenevolentAI, says: It’s a fairly complex task trying to harmonize across maybe hundreds of different sources of unstructured data and bring it together. But what you can do is use [unstructured data] to seed natural language processing systems, [and] use those trained AI systems to read literature, extract similar kinds of facts and information, and then pull it together into a form that is servable. To bring the science of deep learning recommender models (DLRMs) to pharma’s drug discovery process, SambaNova claims they put this data into a model that works like the human brain. The company purports that it built a DLRM that made connections in the data. The DLRM would support the giant drug discovery firms as they navigated the laborious, time-consuming, resource-intensive road that took targets to prescription. We’ll examine the process below. Step 1: Knowledge Graphs- SambaNova claims to have organized the data the way our brains see it—by association. First, project members created a collection of interlinked descriptions of objects, events, and concepts through linking and semantic metadata. This process allowed SambaNova to provide a framework for data integration, unification, analytics, and sharing.
- Next, SambaNova’s NLP team claims to have used PyTorch to define and train the biomedical text-mining algorithms to find the right data and patterns and trends in that data. The team then fed this data into a knowledge graph. The knowledge graph scrutinized the data like a human scientist would, by looking for connections between genes, proteins, diseases, and compounds.
- SambaNova then claims to have used the knowledge graph to train the DLRM to find the best targets in the drug discovery pipeline.
Results
“If you can turn a two-year process into two months or three weeks,” reflects Fox, “how far could you go with an AI-powered recommender model before you need to move to animal or human trials? How much can you speed up those first missteps? Improve the success rate?” DLRMs and knowledge graphs potentially speed up the drug discovery process. They also potentially enable AI to make new connections, discover new pathways, and test ideas without incurring the traditional costs of experimentation. The concept behind SambaNova’s AI-informed recommender models appears to be to enable the collaborative efforts of hundreds of researchers – and decades of experience and observations—to be in the digital room, opining on the viability of new targets. SambaNova’s DLRM solutions appear to allow the pharma industry to identify viable drug discovery targets faster and more efficiently across multiple application scenarios. With AI adoption accelerating in pharma, SambaNova seems to be well-positioned to help the drug discovery process players navigate the challenges of bringing these technologies to the next level.Related Emerj Resources
- Article: AI at Johnson & Johnson – Current Investments
- Interview: The Future of Pharma Data, AI, and Drug Development – with Glen de Vries of Medidata
