AI is transforming drug discovery by rapidly screening molecules and predicting properties..Early clinical trials show AI-designed drugs have higher Phase I success rates..But success drops in Phase II, aligning with traditional industry averages..Bias in training data can limit how well AI generalizes across populations..Poor data quality or missing information can lead to unreliable predictions..Algorithmic bias may favor familiar chemical families over novel compounds..Overreliance on benchmark datasets risks inflated performance estimates..Black-box models raise regulatory concerns if predictions lack transparency..Fixing bias needs diverse datasets, explainable AI, and rigorous validation..Read More Stories.Join our WhatsApp Channel to get the latest news, exclusives and videos on WhatsApp