To date, despite the increasing number of clinical trials, millions of scientific articles and data, knowledge gaps are prominent and hamper biological, therapeutic and medical discovery. This complexity impedes the comprehension of biological mechanisms, affects the chain of drug discovery and later steps including pharmacovigilance and toxicology.
We developed a multi-modal database that regroups biological, medical and therapeutic data by an exhaustive sourcing, an expert data cleaning and an AI based inference of missing relationaships. This knowledge base is combined with a modeling of pharmacological or gene expression perturbation, to generate disruption models accounting for phenotypical alterations.
We have one of the largest signaling database dedicated to Human medecine and therapeutics. Complementary, our AI approach allows the prediction of missing relational data to overcome actual knowledge gaps and involves multiple stages of expert validation. Our relational maps are combined with top of the edge large scale modeling of biological signaling propagation and perturbation observed during therapeutics tests or in multitude desease progression. Altogether, our strategy provides the customer a fast and convenient, yet exhaustive, tractable and personalized way to predict signaling outcomes after signaling perturbations.
We already have in hand an exhaustive multi-modal, and half billion data knowledgebase, as well as a multi-modal and multi-scale dynamic visualization platform to help customers to address therapeutics signaling issues in a personalized dependent manner via democratized access to artificial intelligence.
We are providing a list of validated or foreseen applications below:
– I. (Pre)-clinical applications:
Mechanism insights of molecules actions. This includes accredited or not drugs, compounds that have passed any (pre)-clinical phases, toxics as well as theoretical structures
Target discovery , lead identification and validation (single vs combinatorial, w/o repositioning), therapeutics suggestions.
Clinical trials design (toxicology, personalized medicine, chrono-therapeutics)
– II. Post-market applications:
Explanation of observed secondary effect
Patient trajectory risk management prediction
– III. Business intelligence applications:
Survey of potential competitors and collaborators
Trending of research interests,
Identification of hot and cold spots in therapeutics research