The data fragmentation problem holding drug discovery back

The article “The data fragmentation problem holding drug discovery back” discusses how data silos and disconnected systems across the drug discovery workflow slow scientific progress. In early research, work moves through the Design–Make–Test–Analyse (DMTA) cycle, generating important data at each stage. But most labs store this information in separate platforms, making it hard for teams to see the full picture and make faster decisions.

Sean McGee, Director of Product at Certara, argues that better integration of data infrastructure and AI tools can unify workflows, streamline decision-making, and help teams iterate more effectively. AI and automation can support experimental planning, interpretation, and guidance, but only if the data flow is continuous and coherent across all stages of drug discovery.