AI-Powered Target Discovery

Identify New Drug Targets And Promising Drug Repurposing Candidates Quickly And Easily

The recording of this webinar is available on YouTube.

The effectiveness of drug discovery nowadays depends heavily on the variety and volume of relevant biomedical data and the ability to efficiently integrate data from multiple sources, both proprietary and public. More and more often, Al-based solutions are considered crucial for the evolution of the pharmaceutical industry. They enable biotech and Pharma companies to apply advanced algorithms to generate new drug candidates with a higher probability of successful clinical development. 

Leveraging Al and graph database technology, Ontotext’s Target Discovery Solution addresses specific client requirements that align with the therapeutic target and reduce R&D timelines and spending. Leading industry analysts expect Al-based tools and capabilities to remain a primary product evolution focus area among leading drug discovery focused companies.

The demonstration will showcase how to use Ontotext’s Target Discovery Solution to: 

  • Accelerate new candidate discovery leveraging an interconnected network of more than 5 billion facts about genes, proteins and diseases derived from relevant public reference terminologies, ontologies as well as scientific literature;
  • Visually explore how two or more biological entities are connected and generate valuable insights for your research teams to uncover hidden relationships between targets and diseases, and enable new hypotheses building;
  • Leverage linked research data from various structured and unstructured sources to accelerate new candidate discovery;
  • Mine the huge knowledge network with predefined complex graph patterns;
  • Automatically evaluate hundreds of potential candidates based on custom criteria and graph algorithms.

Ontotext AI powered Target Discovery solution has the following building blocks:

  • LinkedLifeData Inventory;
  • Knowledge graph enrichment via NLP;
  • Configurable semantic search and analytics layer;
  • Ontology and instance management as well as data cataloging with integrated metaphactory.

Who is this webinar for – various business and technical roles in the following functions: 

  • Drug R&D and Target discovery
  • Translational medicine/science
  • Computational sciences

Expected duration: 

  • 45 minutes presentation and demo
  • 15 minutes Q&A session

About The Speaker

Martina Markova

Martina Markova

Business Analyst, Life-sciences, Pharma, Healthcare

Martina is helping Ontotext’s life sciences customers advance their early drug R&D processes by leveraging knowledge graphs, linked data and AI. Prior to joining Ontotext as business analyst, she gathered rich experience in the clinical research industry and helped speed up new treatment delivery to patients at a global scale. She holds a master’s degree in medical biology from the Technical University Munich.

Ilian Uzunov

Ilian Uzunov

Sales Director Life-sciences, Pharma, Healthcare

Sales Director Life-sciences, Healthcare and STM Publishing. Ilian is results-oriented and highly motivated professional with more than 19 years’ experience in managing sustainable and value driven business operations in multiple industry verticals: Publishing (with focus on STM), Pharma and Healthcare, Finance, Government. Ilian has proven track of successful international business development and sales activities building and nurturing long-lasting customer relationships. Ilian is helping business leaders make an easy and cost-effective first step leveraging the power of semantic AI.