AI shows potential to accelerate the drug development pipelines
AI covers target finding, ligand design, retrosynthesis and etc., of our pre-clinical research.Learn more
We arranged pipelines in aging-related diseases, metabolic disorders and cancers.Learn more
Big Data fulfill the AI's requirements need to be generated for exploring AI's potential.Learn more
We have built chemistry lab and animal facilities to support the innovative findings.Learn more
Unlimited possibilities have been revealed in small molecules' exploration...
36Kr discussed the application of AI in target finding, drug design and etc with founder team of Gigaceuticls ...
Our mission and vision
A lot of patients still suffer because of lack of effective drugs. According to our experience, most of diseases can be treated, at least at animal level, from even a relatively small library (~ 4, 000 natural products or active compounds). Given the vast chemical space, any disease, even for cancer, should find a cue or multiple cues. High throughput screening and target-based drug design have achieved big success. Yet there are a lot of room for improvement.
We combined the wet lab verification with recently fast developed deep learning to solving the problems in drug development. Deep learning (part of AI) provides us an overall and comprehensive understanding the similarities of chemicals, chemical-protein interactions, properties of chemicals, chemical gene relationships and etc. Thus, AI will eventually take over the responsibility for novelty disclosure, target finding, ligand design and etc. AI will bring more powerful pre-clinical candidates for further studies.
Represent in Deep and Understand Profoundly
Through iteractively algorithm development and experimental verification, we were able to event and sharp the cutting-edge system and integrate the dry and wet lab internally.