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Machine Learning In The Pharmaceutical Industry

Accelerate R&D and improve testing with increased investment in machine learning

Medicines are becoming increasingly more difficult to develop causing soaring R&D costs. Leverage your data to overcome these complex challenges by accelerating the development and introducing cutting edge technology. Companies can leverage machine learning to save money and time in R&D, manufacturing, and supply chain management.


Clinical trial recruitment

Retain trial participants, and provide better testing with machine learning. Analyze the EMRs to find the most appropriate candidates for participation in a trial, and use their data to predict participant churn and outcomes to avoid potential complications.


Significantly cut down the time it takes to discover new drugs and medicines. Use advanced NLP to analyze existing research and extract important findings. Rapidly pre-process for saved time on testing on incompatible compounds.

Manufacturing optimization

Increase production efficiency with machine learning applications. Run statistical analyses on your data to optimize processes and increase quality. Reduce risk with better consumer safety.

Pharma market analysis

Improve marketing and communications of drugs with advanced insights on market trends. Use deep learning and machine learning to better understand consumer behavior and accurately position medications for the right audience

Personalized medicine

Become a pioneer in Personalized Medicine using machine learning to analyze patient EHRs. Machine learning models can predict how patients will interact with a vast range of substances, which in turn allows pharma companies to rapidly test and design the most effective drug.


Data integration and AI mining of pooled drug-CRISPR screens identifies novel targets and resistance to cancer treatments

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How to personalise new medicines with ML

Automated Image Labeling for Medical Imaging AI

Lessons Learned: How to Make Artificial Intelligence and Data Science Enterprise Ready

Building a Semantic Enterprise-Scale Knowledge Graph for Pharmaceutical Brands