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An iterative process that uses two machine learning models to generate the best inhibitors for a target protein to help reduce the time and cost of the drug discovery process

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PharmaceuticAI

Drug discovery is a very long and expensive process. The average time from FDA application to approval of drugs is 12 years, and the estimated average cost of taking a new drug from concept to market exceeds $1 billion. Of up to 10,000 compounds tested, only one may end up becoming a drug that reaches the market.

PharmaceuticAI was developed to help make this process more time/cost effective, via an iterative process that uses multiple models to generate the best inhibitor for a given target protein.

Data

Drug-like Compounds Dataset - ChEMBL (a manually curated database of bioactive molecules with drug-like properties)

IC50 Dataset - Davis Dataset and KIBA Dataset

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An iterative process that uses two machine learning models to generate the best inhibitors for a target protein to help reduce the time and cost of the drug discovery process

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