The technology firm Euretos has provided free access to its artificial intelligence platform for academic researchers to further support early disease and drug research. This ensures access to researchers to an extensive machine-read knowledgebase, one focused on molecular disease and drug research.
The Euretos integrated data, toolset and workflows that have long been used by the corporate research community in drug development. With a change to policy, the resources have now made available to the academic research community.
Given that academia are generally focused on early disease research, the reason for opening up the AI platform is the contribute to early investigative efforts.
The expansion to the platform over the past few years reflects growing molecular diversity together with more significant manipulation and understanding of biological systems. By collating these, the platform offers an integrated view of publications and relevant research data.
As an example of what can be delivered with the platform, users are able to create gene sets from literature and overlay these with the results of wet-lab experiments. From there, biologists are able to infer the molecular pathways that underpin experimental results.
The pathways of drugs present important information for understanding the mechanisms of drug action and metabolism. In addition, producing pathways allows for drug repositioning, the process that finds new therapeutic indications for approved drugs and experimental drugs that fail approval in their initial indication.
For example, anticancer target drugs specifically bind and inhibit molecular targets. These pathways play important roles in cancer development and progression, and they are deeply implicated in intracellular signaling pathways. Unravelling these is central to new drug development.
With the added benefit of AI, artificial intelligence can assist in structure-based drug discovery. For example, by predicting the three-dimensional protein structure of a substance accordance with the chemical environment of the target protein site. In this way, AI can help to predict the effect of a compound on the target.
The drug efficacy outcome can be enhanced by determining safety considerations, which is of importance before embarking upon synthesis and later production.
On offer with the platform are proactive search capabilities, intuitive analytics functions, visualised relation maps, and an array of specific workflows designed to produce data-driven disease.
