Release Notes#

Release 2.2.0#

Summary#

This release addresses security vulnerabilities, integrates improved model checkpoints trained on an expanded dataset, and introduces telemetry control capabilities to enhance user experience and system reliability.

Key Features#

  • Security: Addressed all CVE (Common Vulnerabilities and Exposures) issues

  • Enhanced Model Checkpoint: Integrated new model checkpoint with improved docking accuracy, trained on an expanded dataset that combines the PLINDER dataset with the SAIR (Structurally-Augmented IC50 Repository) dataset. SAIR (Structurally Augmented IC50 Repository), is the largest public dataset of protein–ligand 3D structures paired with binding potency measurements. SAIR contains over one million protein–ligand complexes (1,048,857 unique pairs) and a total of 5.2 million 3D structures, curated from the ChEMBL and BindingDB databases and cofolded using the Boltz-1x model. By providing this unprecedented scale of structure–activity data, SAIR enables researchers to train and evaluate new AI models for drug discovery by bridging the historical gap between molecular structure and drug potency prediction.

  • Telemetry control: NIM Telemetry helps NVIDIA deliver a faster, more reliable experience with greater compatibility across a wide range of environments, while maintaining strict privacy protections and giving users full control.

    Benefits:

    • Enhances performance and reliability: Provides an anonymous system and NIM-level insights that help NVIDIA identify bottlenecks, tune performance across hardware configurations, and improve runtime stability.

    • Improves compatibility across deployments: Helps detect and resolve version, driver, and environment compatibility issues early, reducing friction across diverse infrastructure setups.

    • Accelerates troubleshooting and bug resolution: Allows NVIDIA to diagnose errors and regressions faster, leading to quicker support response times and higher overall availability.

    • Informs smarter optimizations and future releases: Real-world, aggregated telemetry data helps guide the optimization of NIM runtimes, model packaging, and deployment workflows, ensuring updates target the scenarios that matter most to users.

    • Protects user privacy and data security: Collects only minimal, anonymous metadata, such as hardware type and NIM version. No user data, input sequences, or prediction results are collected.

    • Fully optional and configurable: Telemetry collection is disabled by default. You can toggle telemetry at any time using environment variables.

    Configuration:

    • Set NIM_TELEMETRY_MODE=0 to disable telemetry (default)

    • Set NIM_TELEMETRY_MODE=1 to enable telemetry

    For more information about data privacy, what is collected, and how to configure telemetry, refer to:

Release 2.1.0#

Summary#

  • Improved reporting on details with failed docking requests.

  • Removed TritonServer dependency.

Release 2.0.1#

Summary#

  • Enhanced docking accuracy from the new DiffDock score model trained by PLINDER dataset.

Release 1.2.0#

Summary#

  • Added text file support with multi-line SMILES content as the ligand input for batch-docking.

  • Added timeout support and batch-docking safe-termination.

  • Enhanced performance by adaptive (memory-safe) batch-sampling.

  • Integrated cuEquivariance library.

Release 1.1.0#

Summary#

  • Added multi-molecule SDF file format support for batch-docking.

  • Integrated cuGraph-EQ library (predecessor of cuEquivariance) to accelerate tensor-product operations.

Release 1.0.0#

Summary#

  • This is the first GA release of NVIDIA NIM for DiffDock.