What are the main challenges of using AI in drug discovery?

What are the main challenges of using AI in drug discovery?

Some key challenges include data quality, integration of diverse data sources, and the need for interpretability in AI models.

How does data quality affect AI in drug discovery?

Data quality is crucial because AI models rely on accurate and comprehensive data to make predictions. Poor data can lead to misleading results.

What about the integration of diverse data sources?

Integrating diverse data sources, like genomic, clinical, and chemical data, is complex but essential for a holistic understanding of drug interactions.

Can you explain the need for interpretability in AI models?

Interpretability is important because stakeholders need to understand how AI models make decisions, especially in critical areas like healthcare.

What advancements are being made to overcome these challenges?

Advancements include improved algorithms, better data management practices, and collaborative platforms that enhance data sharing among researchers.