Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?
This work addresses a gap in benchmarking for drug design researchers, though it is incremental as it focuses on comparative evaluation rather than introducing new methods.
The paper tackled the lack of cross-algorithm comparisons in structure-based drug design by establishing a benchmark evaluating sixteen models across different algorithmic foundations, finding that 1D/2D methods achieve competitive performance compared to 3D-based methods and that AutoGrow4 dominates in optimization ability.
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.