CancerAppy is a biotech specialized in the field of cancer research, focused on the discovery and development of new therapeutic targets, based on its own artificial intelligence methodology
We have developed solutions that allow us to be more efficient and precise in the discovery of small molecules.
We work in the early stage of the Drug Discovery Value Chain.
From the initial analysis of the Target to the preclinical phase
Target ID & Validation
Using artificial intelligence algorithms, we work on identifying new potential oncological targets.
Hit discovery
We found potential hits from extensive libraries containing up to 1B of compounds in a fast way.
Hit-to-Lead
We sieve the results obtained based exclusively on scientific criteria to eliminate negative results and reduce the number of false positives.
Lead Optimization
We optimize the structure of the selected lead to improve its on-target activity while reducing its off-target interactions.
Preclinical Development
Each of the potential leads found in silico is tested in vitro internally, to validate the computational results.
Thanks to our deep knowledge and expertise in oncology and AI, we use a massive amount of multimodal data in our state-of-the-art AI algorithms, helping to identify new therapeutic targets, identify lead compounds, and determine the mechanism of action of a given compound. as well as possible drug combinations.
Contact us
We would be happy to talk to you.
News
Latest news about CancerAppy…
CancerAppy to Speak at ACCESS CHINA Partnering Forum
We are glad to announce that Luis Martin, the Chief Executive Officer of CancerAppy, will be presenting at the ACCESS CHINA Partnering Forum –...
China health innovation ecosystem
A Cancerappy delegation has participated in a roadshow of European companies to learn about the innovative health ecosystem in the Shanghai and Wuxi...
CancerAppy Contributes to Innovative Scientific Paper on ADCs in Clinical Use
CancerAppy pioneers in ADC research, contributing to a transformative scientific paper. Our team's dedication unveils insights on payload...
Grant CPP2021-008597 funded by:
Grant TQ2020-011486 funded by:
Subvencionado por el CDTI
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