Drug discovery faces challenges due to the vast chemical space and complex drug-target interactions. This paper proposes a novel deep learning framework TD-GPT for targeted drug molecule generation. TD-GPT comprises a linear Transformer for drug-target affinity prediction, an affinity-enhanced protein encoder using sequences, and a target-specific attention module in the molecular Transformer decoder. Experiments demonstrate TD-GPT’s efficiency in generating valid, novel molecules with high affinity and specificity for desired targets without target fine-tuning. The model provides a new paradigm for accelerated, cost-effective drug discovery.