Pharmaceutical companies are facing soaring development costs and prolonged product development times that are driving the industry to search for Artificial Intelligence and graph tools to ease these pressures. These technologies have proven advantageous, allowing the industry to meet needs in the consumer environment more efficiently and streamline the drug discovery process.
Researchers in bio- and cheminformatics, scientists, and immunotherapists can now use AI-powered graph intelligence to synthesize, optimize, and retarget molecules to design better pharmaceuticals and safer clinical trials. A major challenge to timely life-saving insights is the organization of the data and its impact on the speed and accuracy of generating and testing hypotheses. Until relatively recently, researchers needed extensive computational skills and knowledge to query and explore databases.
Graph intelligence is allowing for more collaborative investigation between researchers and employs FAIR principles: Findability, Accessibility, Interoperability, and Reusability. Volumes of data stored in a myriad of databases and publications has resulted in a wide spread of clinically relevant knowledge that Katana Graph’s analytics technology is capable of refining for use in the drug discovery process. Graph intelligence enables the user to crowdsource the curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.
Biomedical researchers around the world have shared data regarding imaging and sequencing, assays, chemical compounds and their protein targets, and biological molecules. But collaborative research is still hampered by the logistical and technical challenges of searching, integrating, and visualizing heterogeneous data. While there is no lack of information to formulate a hypothesis, findability and interoperability among this vast array of data is a challenge.
The Katana Graph Intelligence Platform helps biopharma companies use graph algorithms to discover new opportunities and efficiencies throughout the life sciences value chain, from virtual screening of drugs to managing healthcare providers participating in clinical trials.
While there is a tremendous amount of potential for breakthrough advances in drug discovery, there is a lack of technological know-how to bring the data to life quickly. High levels of computing power and scaling are required for predictive/AI analytics to synthesize volumes of data stored in many different forms, including structured and unstructured formats.
Using a graph AI with graph neural networks, Katana provides a platform for thorough, efficient analysis of applications vital for bioinformatics and cheminformatics such as node classification, link prediction, and recommendation systems. With easy-to-use and scale-out packages for learning large-scale knowledge graphs, we enable life sciences enterprises to accelerate time-sensitive, data-driven business decisions from R&D to clinical trials.
Katana Graph’s unified intelligence platform enables life sciences knowledge workers to store, query, mine and develop AI models using heterogeneous data sources to drive innovation across a wide range of information types, use cases, and industries.
Whether structured or unstructured data, Katana Graph can greatly improve the insights and opportunities uncovered from your organization’s data. Click to schedule a meeting.