Drug discovery involves finding molecules that bind to a target and have acceptable safety profiles. Machine learning can help prioritize candidates by predicting binding affinity, toxicity, or pharmacokinetic properties.
Models are only as good as their data. Bias, limited chemical diversity, and noisy labels can lead to overconfident predictions that fail in experiments.
Hybrid workflows combine ML screening with lab validation, using new experimental results to iteratively improve models and reduce wasted effort.