is crucial to increase speed and accuracy of your drug development process, and we can help you adopt it!
Why go for AI in Pharma and Biotech Industries?
AI applications can reduce time and optimize various areas in the drug development process.
Tabs
Cognitive Molecule Research
AI can sort and compare millions of potential small molecules to virtually build synthesizable molecules with desired properties. This eliminates the trial-and-error process which is costly and time consuming.
Predictive Analysis
Predictive models can analyze historical information on how a particular target behaves when interacting with other proteins. This helps in selecting promising therapeutic molecules with potential to treat specific diseases.
Site Design And Patient Selection
Using AI, ML and NLP the vast health care data can be analyzed to identify the most relevant protocols for regulators, payers and patients in the site design.
and ML can identify the sites with the highest recruitment potential and suggest appropriate recruitment strategies.
Automated Data Flow
Automating data flow across the clinical trial life cycle creates a structured, standardized digital data elements which are then interpreted and auto populated to required reports and analyses.
Early Warning System
AI can analyze thousands of research papers and publicly available competitor information to provide a list of threats and opportunities, giving you a strategic advantage over others.
Market Analysis
Applying AI to your market research database ensures all team members have access to the entire market analysis. Add a layer of chatbot and your team can search data by simply asking questions.
Personalized Patient Engagement
AI can increase the effectiveness of patient engagement programs. Leveraging the varied datasets such as prescriptions, medical data, historical engagement data AI can predict and prioritize patients who are apt for your drug.
Automated Adverse Event Reporting
Using AI technologies such as optical character recognition and NLP, the intake of adverse event reports can be automated and centralized. This reduces manual efforts and helps to expedite the investigation process.
CASE STUDY
We helped a global pharma company to save time and improve the success rate of their clinical trial protocols using AI and ML
A global pharmaceutical company clinical trials department was putting in a lot of manual effort in monitoring significant quality events (SQEs’).
Partner with us and explore the endless possibilities of AI and ML
Snowflake and Dataiku partnership
Pharma domain expertise
Technical
expertise
If you want to see our services in action, then fill up the form below and our experts can give you a demo and answer your questions.
Contact us today!
John Gregory
Strategic Advisor
John has 20+ years in digitalizing portfolio management, pre-clinical and R&D departments. His expert advice helped many of our clients to succeed in adopting technology.
Peter Green
VP Analytics
Peter has 18 years of experience in pharmaceutical data and analytics, specifically in R&D project and portfolio management. Ask Peter about digital data analytics strategies and technologies.