  Virtual screening (VS) is an essential technique for understanding
biomolecular interactions, particularly, drug design and discovery. The
best-performing VS models depend vitally on three-dimensional (3D) structures,
which are not available in general but can be obtained from molecular docking.
However, current docking accuracy is relatively low, rendering unreliable VS
models. We introduce sequence-based virtual screening (SVS) as a new generation
of VS models for modeling biomolecular interactions. The SVS model utilizes
advanced natural language processing (NLP) algorithms and optimizes deep
$K$-embedding strategies to encode biomolecular interactions without invoking
3D structure-based docking. We demonstrate the state-of-art performance of SVS
for four regression datasets involving protein-ligand binding, protein-protein,
protein-nucleic acid binding, and ligand inhibition of protein-protein
interactions and five classification datasets for the protein-protein
interactions in five biological species. SVS has the potential to dramatically
change the current practice in drug discovery and protein engineering.
