This research report reveals growing adoption of data science in drug discovery, with 78% of biopharma companies planning extensive use by 2028.
Drug discovery teams are rapidly adopting data science, with 60% using these approaches and 78% planning to expand within three years. However, many organizations may overestimate their data quality and integration capabilities, risking the success of AI and machine learning initiatives. A recent survey highlights a disconnect between ambitious data science goals and the foundational infrastructure required to achieve them.
This report provides insights into how biopharma companies use data science in biomarker discovery, target validation, and preclinical studies. It identifies gaps in priorities and offers actionable strategies to optimize data science investments while avoiding pitfalls that could hinder discovery outcomes. Explore:
- A survey of 150 biopharma executives on data science adoption trends
- Analysis of impactful use cases and data types in drug discovery
- Expert insights on underestimated data quality challenges
Offered Free by: BioPharma Dive’s studioID and Qiagen
See All Resources from: BioPharma Dive’s studioID and Qiagen
