Algorithm379
In silico Therapeutics

PIPELINE & INNOVATION


Our major focus is the metabolic and immune diseases that can be targeted with compound, RNA, and peptide based therapeutics.






We are also building precision medicine platforms that are part of a larger network connecting clinical, experimental, and drug repurposing data.






Therapeutics developed for genetically validated targets are more likely to progress through clinical trials. Moreover, by integrating data from transcriptomics, genomics, proteomics, as well as metabolomics studies, we can gain a better understanding of how a specific phenotype or molecular perturbations lead to the pathology and how this state can be reversed and/or pathological burden alleviated. These lessons can also be applied when developing a precision medicine platform to achieve a better therapeutics selection.


Our approach relies on extensive biological data integration to identify and prioritise targets. This is achieved by using the combination of public data and our in-house curated atlases as well as knowledge-bases.




Using experimental evidence with established datasets we can apply machine learning/AI driven target characterisation in the context of the disease biology.


By scoring and ranking targets based on disease associations, we can further prioritise genes, proteins, or entire networks that could be therapeutically modulated.


When a pathological state network is established, we offer a system biology based modeling to predict the outcome of various perturbations within the network and infer what pharmacological intervention could be the most promising.




Expert developed libraries for promising target search