Background

Developing a new drug is a long and expensive process that requires the work of many people. Through trial and error, biologists need to examine and test tens of thousands of compounds before making the right selection of ingredients. Current testing techniques are slow and often require manual data comparison and laborious searching of related data. Pharmaceutical companies need improved efficiencies of research and clinical trials so the impact of drug therapies can be discovered and evaluated faster.

Opportunity

Efforts and resources can be saved by creating better predictive methods that narrow down the drugs tested clinically based on compound features. Leveraging artificial intelligence (AI) and machine learning (ML), the data of existing compounds can be used to design new compounds and help biologists structure a drug so it will better bind to target molecules. Realizing a faster time-to-insight on the efficacy of potential drug remedies can save pharmaceutical companies an immense amount of time and billions of dollars.

Impact

Implementing a fast, accurate, reproducible, and cost-effective drug discovery framework is the power needed to go-to-market faster and save billions of dollars for the industry. See how Anaconda Enterprise can improve data science workflows in pharmaceuticals by contacting us.