Spatial Needleman-Wunsch: A Deterministic Dynamic Programming Framework for Molecular Docking

Abstract

Most molecular docking tools today rely on stochastic sampling or heuristic optimization. They can be effective, but reproducibility is often hit or miss, and theres no guarantee the best binding pose has been found. This paper introduces Spatial Needleman-Wunsch, a deterministic dynamic programming framework adapted from sequence alignment and applied to three-dimensional molecular structures. Ligands and protein cavities are voxelized into grids; chemical interactions are scored with compatibility matrices inspired by BLOSUM and PAM; and optimal alignments are computed exhaustively. The result is a docking method thats reproducible, interpretable, and mathematically grounded. The framework supports Boltzmann ensemble analysis, Pareto trade-off modeling, conformational sampling, and adaptive scoring. Proof-of-concept tests on synthetic systems and real targets like HIV protease show perfect reproducibility and meaningful chemical discrimination. This approach eliminates random variability while making interaction scoring fully transparent. It opens the door to principled docking exploration, something essential in drug discovery and structural biology.