  In recent years, AI models that mine intrinsic patterns from molecular
structures and protein sequences have shown promise in accelerating drug
discovery. However, these methods partly lag behind real-world pharmaceutical
approaches of human experts that additionally grasp structured knowledge from
knowledge bases and unstructured knowledge from biomedical literature. To
bridge this gap, we propose KEDD, a unified, end-to-end, and multimodal deep
learning framework that optimally incorporates both structured and unstructured
knowledge for vast AI drug discovery tasks. The framework first extracts
underlying characteristics from heterogeneous inputs, and then applies
multimodal fusion for accurate prediction. To mitigate the problem of missing
modalities, we leverage multi-head sparse attention and a modality masking
mechanism to extract relevant information robustly. Benefiting from integrated
knowledge, our framework achieves a deeper understanding of molecule entities,
brings significant improvements over state-of-the-art methods on a wide range
of tasks and benchmarks, and reveals its promising potential in assisting
real-world drug discovery.
