Real-World Assessment of Machine-Learned Docking Using Bioassay-Derived Benchmarks.

The rapid expansion of compound libraries has significantly advanced drug discovery, especially through ultralarge library screenings that provide access to vast chemical spaces. However, the sheer scale of these libraries introduces substantial challenges in the early stages of drug discovery. While it is true that searching larger libraries can improve the hit rate of a virtual screening campaign, as the available chemical space has increased to over 64 billion molecules, identifying relevant information becomes a complex and time-consuming task. Machine learning (ML)-based docking methods and scoring functions offer a potential solution by providing increased speed and scalability. However, much of their reported success is based on benchmark data sets that rely on constructed decoys and ligand sets, which often have hidden biases that can artificially inflate performance and fail to capture the challenges of real-world screening. In this work, we systematically evaluate the performance of a popular ML-based docking method, DiffDock-Pocket, on high-throughput screening (HTS) data sets derived from the PubChem BioAssay database, a premier source of bioactivity data. By using HTS data sets as a more realistic benchmark, we aim to provide a clearer picture of how ML models perform in practical virtual screening scenarios compared to traditional physics-based docking approaches. Our work highlights the strengths and limitations of current ML methods and offers insights into their reliability and applicability to prospective applications in drug discovery.