Using data and AI to power the discovery of GalOmic™ therapies
HepNet™'s proprietary computational and AI approaches utilise extensive data sources to enhance our therapeutic discovery, from novel gene target identification to
AI-driven drug design.
Data-driven discovery
HepNet™ boasts a continually expanding foundation of data, combining proprietary insights from preclinical experiments, licensed datasets, and meticulously curated public data. This world-class hepatocyte-specific knowledgebase is the backbone of HepNet™'s powerful computational and AI approaches. By leveraging this extensive data foundation, including our proprietary hepatocyte-focused Knowledge Graph, HepNet™ drives the discovery of our innovative GalOmic™ RNAi therapies. This robust data integration ensures the integrity of our outputs, empowering us to make better medicines faster.
14M
hepatocyte specific data points
20,000
coding and non-coding genes in knowledgebase
1,300
hepatocyte associated diseases in knowlegebase
We focus on tackling the hardest challenge, which is being overlooked: human biology.
Identifying transformational targets
Utilising cutting-edge network analytics and AI within HepNet™, we delve into the complexities of biology to identify novel gene targets for drug discovery.
We go beyond the surface; our computational models provide a comprehensive, holistic view of the biological systems we are targeting. This approach enhances our ability to identify gene targets that can truly make a difference in treating diseases.
Upon identifying potential targets, each target-indication pair undergoes thorough assessment, scrutinising their biological relevance and developability. By leveraging AI and large language models (LLMs), we enhance the speed and scale of our evaluation, driving the growth of our target pool and allowing us to nominate targets with confidence. This rigorous process not only allows us to build a desirable risk balance portfolio but also accelerates our transition from discovery to development, ultimately benefiting patients.
Streamlined drug design
HepNet™ revolutionises drug design with its AI-powered siRNA design and efficacy prediction model. Trained on siRNA sequences with GalOmic™ modification patterns, HepNet™ can generate potent, long-acting GalOmic™ siRNA sequences entirely in silico.
This advanced capability allows us to bypass the traditional in vitro screening process, significantly cutting down preclinical development timelines and costs. By streamlining these critical steps, HepNet™ further accelerates our ability to develop innovative treatments.
Related pages
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