Leveraging AI and our platform technology, we are developing our internal drug program, with an initial focus on lung fibrosis and IBD. Our team has developed a machine learning model capable of predicting the binding affinity of small molecules to defined targets. Utilizing the model, we have virtually screened over 2,000 drug compounds and selected the top-ranked candidates for efficacy testing in our pulmonary fibrosis model.
Leveraging AI and our platform technology, we are also developing our own internal drug program.
Our initial focus is on lung fibrosis with a well known target, TGF-beta, which has been shown to a potent regulator of fibrosis progression.
We develop our own machine learning model that can predict the binding affinity of small molecule to this TGFbeta target.
Using the model we virtually screening over 2000 drug compounds and selected the top one highest ranked compounds for subsequent efficacy testing on our pulmonary fibrosis model.
In this effort, our primary focus is to establish and validate the feasibility of our discoveryworkflow.
In the future, our target and compounds library can be easily changed and adopted for other diseases such as IBD.
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