Drug Discovery
Use Case Family:
GenAI, Automation, Predictive Analytics & Forecasting
Business Domain:
R&D
Processes:
Product Design
Challenge:
The drug discovery process is slow, expensive, and often uncertain. Traditional methods rely on trial-and-error approaches, requiring years of testing and billions of dollars in investment. Predicting the efficacy of new drug compounds is complex, as minor molecular modifications can drastically affect their performance. This delays drug development and limits the speed at which new treatments reach patients.
Solution:
Generative AI accelerates drug discovery by designing and evaluating new drug compounds based on existing molecular structures. By analyzing vast datasets of chemical and biological interactions, AI models can predict which compounds are likely to be effective. This approach reduces the need for extensive lab testing and helps identify promising candidates faster. Additionally, AI-powered simulations enable researchers to assess drug safety and efficacy before physical synthesis.
Benefits:
- Faster drug discovery: Reduces the time needed to identify promising compounds.
- Lower R&D costs: Minimizes expensive lab testing and failed experiments.
- Improved accuracy: AI models enhance compound efficacy predictions.
- Better patient outcomes: Accelerates the availability of new treatments.
Target Group:
Potential Industries:
High Risk systems
Potentially high risk accoring to Art. 6; Annex I (11)
with transparency obligations
Disclaimer
The information provided regarding the risk assessment is without guarantee. The complete classification of a use case according to the EU AI Act depends on numerous regulatory and company-specific factors. Therefore, the risk assessment is always case-specific. The risk assessment logic of Casebase is used for this purpose.