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 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.

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.

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.

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.

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.

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.

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.