Structure-based virtual screening confronts a grand challenge in scaling to trillion-ligand libraries for drug discovery. We present SWDOCKP2, a performance-portable virtual screening framework achieving 1.9 trillion ligand-receptor pairs daily across eight targets on the Sunway OceanLight supercomputer with 39-million cores — 10× faster than prior state-of-the-art. Key innovations combine (1) a ligand database optimizer with conformational sorting and merging, (2) multi-receptor grid alignment enabling parallel target screening and SIMD-accelerated trilinear interpolation, and (3) a Sunway architecture emulator for cross-platform efficiency. These advancements bridge computational scalability with novel drug discovery demands, offering a blueprint for next-generation supercomputing in structure-based drug design. Additionally, SWDOCKP2 will generate an unprecedented dataset of predicted protein-ligand interactions, creating a transformative resource for machine learning applications. By addressing experimental data scarcity, this dataset empowers accurate ligand prediction, generative chemistry, and AI-driven drug discovery.

Molecular docking is the process of posing, scoring, and ranking small molecules at the binding sites of proteins to prioritize compounds for experimental testing. It is a widely-used computational method in the drug discovery process. However, it is a highly time-consuming procedure since a receptor may need to find favorable ligand orientations in billions of ligands. UCSF DOCK3.7 is one of the most widely used molecular docking applications. In this paper, we port and optimize UCSF DOCK3.7 on the Sunway TaihuLight supercomputer. To avoid the impact of load imbalance, we employ a producer-consumer strategy that can overlap I/O and computation in order to achieve high performance. Furthermore, we present a new binary file format to replace the mol2db2 file format for ligand storage and adopt xzip rather than gzip to compress ligand files. We show that our file format can reduce I/O time significantly while xzip saves significant storage. For the routines which determine the orientation of a ligand relative to the receptor, we present an improved algorithm to discard geometrically similar orientations. Furthermore, we fuse loops and compress memory usage to store data in fast Local Device Memory (LDM) in order to score ligand orientations with high efficiency. In addition, we propose a number of architecture-specific optimizations. Asynchronous data transfer and vectorization of computation are implemented to take full advantage of the SW26010 processor. Our experiments show that a speedup of 167 can be achieved by using the proposed strategies. Compared to a core of an Intel(R) Core(TM) i9-10900K CPU, our approach achieves speedups of 15 on a SW26010 core group. Furthermore, our implementation achieves strong scalability to hundreds of thousands of heterogeneous cores on the next-generation Sunway supercomputer.

The Janus Kinase 2 (JAK2) plays essential roles in transmitting signals from multiple cytokine receptors, and constitutive activation of JAK2 results in hematopoietic disorders and oncogenesis. JAK2 kinase activity is negatively regulated by its pseudokinase domain (JH2), where the gain-of-function mutation V617F that causes myeloproliferative neoplasms resides. In the absence of a crystal structure of full-length JAK2, how JH2 inhibits the kinase domain (JH1), and how V617F hyperactivates JAK2 remain elusive. We modeled the JAK2 JH1-JH2 complex structure using a novel informatics-guided protein-protein docking strategy. A detailed JAK2 JH2-mediated auto-inhibition mechanism is proposed, where JH2 traps the activation loop of JH1 in an inactive conformation and blocks the movement of kinase αC helix through critical hydrophobic contacts and extensive electrostatic interactions. These stabilizing interactions are less favorable in JAK2-V617F. Notably, several predicted binding interfacial residues in JH2 were confirmed to hyperactivate JAK2 kinase activity in site-directed mutagenesis and BaF3/EpoR cell transformation studies. Although there may exist other JH2-mediated mechanisms to control JH1, our JH1-JH2 structural model represents a verifiable working hypothesis for further experimental studies to elucidate the role of JH2 in regulating JAK2 in both normal and pathological settings.