  The generation of small molecule candidate (ligand) binding poses in its
target protein pocket is important for computer-aided drug discovery. Typical
rigid-body docking methods ignore the pocket flexibility of protein, while the
more accurate pose generation using molecular dynamics is hindered by slow
protein dynamics. We develop a tiered tensor transform (3T) algorithm to
rapidly generate diverse protein-ligand complex conformations for both pose and
affinity estimation in drug screening, requiring neither machine learning
training nor lengthy dynamics computation, while maintaining both
coarse-grain-like coordinated protein dynamics and atomistic-level details of
the complex pocket. The 3T conformation structures we generate achieve
significantly higher accuracy in active ligand classification than traditional
ensemble docking using hundreds of experimental protein conformations.
Furthermore, we demonstrate that 3T can be used to explore distant
protein-ligand binding poses within the protein pocket. 3T structure
transformation is decoupled from the system physics, making future usage in
other computational scientific domains possible.
