  Currently, the field of structure-based drug design is dominated by three
main types of algorithms: search-based algorithms, deep generative models, and
reinforcement learning. While existing works have typically focused on
comparing models within a single algorithmic category, cross-algorithm
comparisons remain scarce. In this paper, to fill the gap, we establish a
benchmark to evaluate the performance of sixteen models across these different
algorithmic foundations by assessing the pharmaceutical properties of the
generated molecules and their docking affinities with specified target
proteins. We highlight the unique advantages of each algorithmic approach and
offer recommendations for the design of future SBDD models. We emphasize that
1D/2D ligand-centric drug design methods can be used in SBDD by treating the
docking function as a black-box oracle, which is typically neglected. The
empirical results show that 1D/2D methods achieve competitive performance
compared with 3D-based methods that use the 3D structure of the target protein
explicitly. Also, AutoGrow4, a 2D molecular graph-based genetic algorithm,
dominates SBDD in terms of optimization ability. The relevant code is available
in https://github.com/zkysfls/2024-sbdd-benchmark.
