DeepCure's AI algorithms analyze a trillion drug candidates from our proprietary virtual library and accurately point out the most promising ones while accounting for the dynamic nature of the target and optimizing the required ADMET properties. DeepCure's AI helps identify novel optimization steps that would escape the eyes of even the most experienced medicinal chemist. These steps improve the success rates of drug candidates in subsequent development stages while reducing time and cost. Whether your lead series is hitting some unexpected barriers, or you are just interested in new approaches to increase the chances of success, we use our vast chemical space and cutting-edge AI to identify novel structures to diversify your portfolio.

MolGen is the first algorithm to fully account for synthesizability while supporting the creation of chemical space in the trillions scale. Trained on a proprietary dataset of chemical reactions, MolGen learned the organic chemistry fundamentals that make MolDB, and enable DeepCure to explore almost every possible drug candidate and select the optimal ones.

Our AI Engine is a platform of predictive models, optimization frameworks and an active learning workflow. In simple terms, we use AI to predict and optimize a drug’s most important properties, such as potency, selectivity, toxicity, to name a few.

Our state-of-the art machine learning (ML) models were designed to overcome the most challenging task for ML in drug discovery – generalizability (aka discovering the unknown). Traditional methods and most algorithms using machine learning can only accurately predict properties of molecules similar to known entities – which of course, have little to no use in discovering novel drugs.

Our AI team has designed a set of statistical methods to clean the data, including a process to normalize and filter data points from the different sources by synthesizing and testing compounds in our internal assets.

DeepCure Series A — DeepCure

DeepCure Series A — DeepCure