AI-Powered Target Discovery
Identify New Drug Targets And Promising Drug Repurposing Candidates Quickly, Easily and with high Confidence:
Accelerate your AI in clinical research at least 10x. Easily and quickly mine relevant scientific information and explore new relationships across vast connected data sources;
Enrich your knowledge with an AI-powered therapeutic target database, automatically extracted from scientific publications, patents and study materials;
Utilize your scientific research advanced algorithms to analyze large and complex network relationships between biomedical entities effectively;
Embrace data-driven decision-making in target assessment and prioritization;
Promote innovation by using a digital discovery platform that adapts to your research and business needs in both new drug target discovery and repurposing of existing drugs;
Book a Demo
Discover previously unknown connections between targets and diseases, quickly generate and assess new ideas to guide drug research and development.
Utilize large amounts of information from various internal and external biomedical sources to speed up the process of discovering and repurposing drugs.
Prioritize targets based on the latest research data by using custom evaluation criteria and algorithms, and get value from both structured and text data.
About Ontotext’s Target Discovery
Discovering the next breakthrough drug candidate is a daunting task, involving sifting through vast amounts of biomedical information scattered across different databases and research publications. This process can be time-consuming and difficult to mine, gather and analyze.
Ontotext’s Target Discovery solves this by combining all the knowledge about biomedical entities, such as genes, proteins and compounds, into a centralized Knowledge Graph. The system democratizes data access to AI in biomedical engineering through powerful search tools, presents it in a user-friendly manner, and provides transparent analytics to aid data-driven decision-making.
Using AI for preclinical research provides fast and easy access to facts generally hidden in millions of scientific publications and patents and predictions about novel relations between genes and diseases. The process of selecting the right candidate is further enhanced by comprehensive data provenance and confidence metrics for AI-driven drug discovery.
How it Works
The building blocks of Ontotext’s AI-powered target discovery solution include:
Leverage pre-configured data sources from Ontotext LinkedLifeData inventory that covers concepts such as genes, proteins, drugs, pathways, clinical trials, scientific literature, patents, etc.
Knowledge graph enrichment via NLP
Automatic extraction of relationships from leading scientific databases, such as Pubmed and Google Patents, to provide a comprehensive understanding of the concepts that are crucial for your research and decision making. Our AI tools provide accurate, reliable, and up-to-date information about millions of biomedical facts.
Data exploration that is easy to use and does not require technical expertise
Design and customization that prioritizes the needs and preferences of the user;
Providing transparent results to a wide range of access points;
Includes metadata with detailed information about the origin and credibility of each result, and allows for customizable ranking options.
Powerful and configurable analytics
Unlock the potential of AI to predict new targets for your research. Utilize our advanced tools that use knowledge of genes, proteins, and diseases to identify new therapeutic targets with precision and efficiency;
Maximize the potential of your data with our ranking and clustering algorithms;
Unlock the full potential of your custom machine learning algorithms by integrating them seamlessly into our platform. Our flexible architecture allows you to plug in your own algorithms to extract even deeper insights from your data;
Who Ontotext’s Target Discovery help?
Scientists and Researchers
Typically, it’s challenging to find relevant information in the vast amount of scientific publications and make sense of both public and internally produced data. Target Discovery helps minimize difficulty in deciding what assays to prioritize based on all available information.
Translational biology professionals
Often, there are risks for Phase II trial failure because drug target is not causally linked to disease etiology, maintainance or progression. Even after safe & effective modulation of the intended target with the intended mechanism of action, there may be no measurable effect on patient outcomes.
A lot of time can be spent finding, accessing, curating and integrating data, before any real analysis can begin. This helps remove the difficulty explaining the challenges to the business stakeholders, who expect large volumes of data to be analyzed with very short turnaround times.