Read about how industry leaders are using Ontotext knowledge graph technology to discover new treatments and test hypotheses.
As much as we know about certain diseases, many remain a mystery and require a deeper dive into the complex biological world to understand the pathology and underlying mechanisms. While patients are still suffering, scientists are advancing slowly towards new drugs, which are both safe and efficient.
To support drug discovery, Ontotext has recently developed a method for gene-disease link prediction, which can help focus research efforts where it would matter most and speed up the drug development process.
The link prediction models are based on the so-called Knowledge Graph Embeddings, which relying on knowledge graph technology. It contains the ground truth about pathological mechanisms, molecules, interactions, drugs and targets. Knowledge graphs help biomedical experts understand the relationships between different concepts better and build a complete picture over the problem of interest.
The challenge is that for some diseases or genes, the information available is still not comprehensive and the pathological processes on molecular level are not well understood. With the help of link prediction models, the graph can be enriched with new information, which is otherwise inaccessible to scientists.
Researchers and medical professionals can benefit from AI-predicted gene-disease prediction in various ways.
Novel target identification is a crucial step in drug discovery and development. The traditional approach to identifying new targets involves extensive experimentation and trial-and-error, which is often time-consuming, costly and may not yield desirable results. However, with the help of AI predicted gene-disease relations, researchers can rapidly identify new biomolecules involved in disease pathology which can serve as potential drug targets.
The predictions generated by Ontotext’s method provide insights into the complex interplay between genes and diseases, enabling researchers to build new hypotheses about targets that are likely to have a significant impact on disease outcomes. This approach can help streamline the drug development process by enabling researchers to focus on the most promising targets, reducing time and resources required for experimental validation.
Deeper disease understanding is critical in improving patient outcomes and developing effective treatments. By understanding the pathological mechanisms of a disease on a molecular level, researchers can identify potential biomarkers that can serve as indicators of disease progression and response to therapy. This information can be used to develop more reliable and fast diagnostic methods, which can help in early detection of diseases.
What’s more, understanding the molecular mechanisms underlying a disease can reveal new therapeutic targets that can be exploited to develop new treatment strategies. Also, a deeper understanding of the disease biology can provide insights into the factors that contribute to disease progression and help identify ways to prevent or slow down disease progression.
Accelerated drug development is a key benefit of AI-predicted gene-disease link predictions. With the help of these predictions, researchers can identify potential targets for drug development more efficiently and accurately. This approach enables researchers to focus on the most promising targets, reducing the time and resources required for drug development.
AI-predicted gene-disease link predictions can also be used to expand the indications for already approved therapeutics. By identifying new disease indications for existing drugs, researchers can repurpose these drugs for new uses, reducing the time and cost associated with developing new drugs from scratch.
To further streamline the drug development process, Ontotext’s Target Discovery Ranking can be used to rank the newly identified targets according to other criteria, such as druggability or importance. This approach helps researchers select the most promising candidates for further wet lab analysis, enabling them to prioritize their drug development efforts on the targets that are most likely to result in successful drug candidates.
The use of AI-predicted gene-disease link predictions and Ontotext’s Target Discovery Ranking can help accelerate the pace of drug development, reduce the risk of drug failure and ultimately improve patient outcomes.
The predicted links between genes and disease relations can help speed up target validation, drug development and provide novel insights into disease biology.
The gene-disease predictions can be used as part of Ontotext’s AI powered Target Discovery. The newly released product helps researchers and data scientists join forces to find new therapeutic targets and automatically validate hundreds of candidates based on custom criteria.
Through Target Discovery, biomedical experts can quickly evaluate the risk, druggability, novelty, importance and relevance of hundreds of preselected therapeutic candidates, all in a matter of minutes. This approach helps speed up the preclinical assessment process by a factor of 10, as reported by customers, and helps de-risk the drug development process by promoting targets, which are more likely to lead to successful treatment.
This powerful solution enables researchers without any technical skills to quickly gain an overview of any disease and understand the pathology, pathways and key players. With the help of advanced analytics, scientists are empowered to build new hypotheses and uncover hidden relationships between any pathological process, chemical structure or biological molecule.