v0.5.0

0.5.0 - 2026-05-13

Summary

nvMolKit 0.5.0 adds three new GPU-accelerated APIs: Torsion Fingerprint Deviation (TFD), pairwise conformer RMSD, and UFF force field optimization. It also introduces a BatchedForcefield Python API for MMFF and UFF with constraints, custom options, and multi-conformer minimization; a low-memory fused Butina clustering path that avoids the O(N²) distance matrix; a Python autotuning framework for the main APIs; and optional device-side output for ETKDG and forcefield optimization. Blackwell / L-class GPUs (including sm_103/B300) are now supported

Contributors

• Kevin Boyd (@scal444)
• Eva Xue (@evasnow1992)
• Alireza Moradzadeh (@moradza)
• Andrei Volgin (@volgin)

Features

• GPU-accelerated Torsion Fingerprint Deviation (TFD) for batch all-pairs conformer comparison (#71)
• GPU-accelerated pairwise conformer RMSD matrix computation by @volgin
• GPU-accelerated UFF force field, supporting all options that the new BatchedForcefield Python API provides for MMFF (#114)
• New BatchedForcefield Python API exposing per-molecule control over forcefield minimization (MMFF or UFF), and through it custom MMFF optimization options (max iterations, energy/gradient tolerances, non-bonded cutoff) (#70)
• Distance and position constraints on forcefield optimization (MMFF and UFF) (#26)
• Multi-conformer minimization in the BatchedForcefield API
• HardwareOptions support for MMFF minimization, matching the ETKDG hardware-targeting API
• Device-side output for ETKDG and forcefield optimization, allowing GPU tensors to flow between nvMolKit calls without round-tripping through host memory (#140)
• Python autotuning library for the main APIs (nvmolkit.autotune), including ETKDG, forcefield optimization, and substructure search, with configuration serialization (#141)
• Low-memory fused Butina clustering that computes Tanimoto similarities on the fly with Triton-backed kernels, avoiding the O(N²) distance matrix and enabling clustering of larger fingerprint datasets on a single GPU (#110)
• Support for Blackwell and L-class GPUs, including sm_103 SASS for B300

Bug Fixes

• Fix latent stream-ordering bug in the MMFF/BFGS minimizer that could race with subsequent operations (#172)
• Fix int32 overflow in substructure pair indexing for batches where numTargets * numQueries exceeds INT32_MAX, which previously caused out-of-bounds writes in hasSubstructMatch and countSubstructMatches (#169)
• Fix shared-memory overflow in the substructure recursive preprocessor caused by an incorrect config setting (#98)
• Fix empty result handling in substructure search with uniquify when all matches were already unique (#112)

Miscellaneous

• pip wheel distribution pipeline (pip install nvmolkit) with manylinux_2_28 wheels for CPython 3.11-3.14 (#15)
• RDKit support range is now 2025.03.1 through 2026.03.1
• Validate batchesPerGpu in HardwareOptions so every consumer gets a clean ValueError instead of a cryptic C++ error from the MMFF / ETKDG layer (#103)
• Validate neighborlist_max_size in butina() before reaching the GPU (#104)
• Validate MMFF atom types up front and report every failing molecule instead of hitting a PRECONDITION assertion mid-batch (#106)

Release v0.4.0

0.4.0 - 2026-02-23

Summary

nvMolKit 0.4.0 adds GPU-accelerated substructure searching, optional stream control across Python APIs, and enhancements to Butina clustering.

Contributors

• Kevin Boyd (@scal444)
• Eva Xue (@evasnow1992)

Features

• GPU-accelerated substructure search with hasSubstructMatch, countSubstructMatches, and getSubstructMatches. Supports batch queries against batch targets with SMARTS-based query molecules.
• Optional stream parameter added to fingerprint generation, similarity, and Butina clustering APIs, enabling explicit CUDA stream control
• Butina clustering now supports optional centroid reporting via the return_centroids parameter (#82)
• Butina clustering performance improved by replacing CPU loops with CUDA Graph conditional nodes (#72)

Bug Fixes

• Fix data races when torch operations immediately followed nvMolKit calls on the default stream (Issue #84). Operations now correctly use the current stream or an explicit stream parameter (#36).
• Fix setup.py compatibility on some Python versions and rework CUDA target detection (#68)

Release v0.3.0

0.3.0 - 2025-12-12

Summary

nvMolKit 0.3.0 adds Butina clustering support, improved performance to MMFF relaxation and conformer generation, and increased compatibility with libraries and compilers.

Contributors

• Kevin Boyd (@scal444)
• Eva Xue (@evasnow1992)
• Xuangui Huang (@stslxg-nv)

Features

• Butina clustering API enabled, using distance matrix input. On an H200 GPU, speedups of 400-1000x can be achieved on datasets up to 60k molecules
• Improvements to BFGS minimizer. Up to 5x speedup compared to nvMolKit v0.2 on batches of small molecules (<20 atoms), with ~10-20% speedup in the general case. Applies to both MMFF relaxation and conformer generation.
• Conda-forge releases now support RDKit versions 2024.9.6 to 2025.9.3

Bug Fixes

• Fixed a bug where synchronizations on the wrong stream could lead to data races in tests (Issue #28)
• Fixed several areas where a memcpy could go out of scope before completing (Issue #28, Issue #29)
• Fixed a bug where ETKDG would exit early with small CPU counts due to an incorrect identification of resource mis-configuration (Issue #31)

Miscellaneous

• (C++) Added support for CUB/CCCL > v2.8
• (C++) Added support for externally specified CCCL
• (C++) Added support for CUDA 13.0

nvMolKit v0.2.0

0.2.0 - 2025-10-24

Summary

nvMolKit 0.2.0 comes with significant usability and feature-completeness improvements to existing functionality. It is also
the first release to have a conda-forge release.

Contributors

• Kevin Boyd (@scal444)
• Eva Xue (@evasnow1992)
• Ignacio Pickering (@IgnacioJPickering)

Features

• Add memory-segmented cross-similarity code, enabling larger datasets on systems with limited GPU memory (#13)
• Support conformer deduplication in ETKDG conformer generation (#14)
• Allow molecules > 256 atoms in conformer generation and MMFF optimization (#16)
• Enable all combinations of (ET)(K)(DG) in conformer generator (#17)

Bug Fixes

• Fix compilation error on C++ build with target=native on Hopper architecture GPUs. (#6)
• Fix lack of device-set cleanup in multi-GPU code (#8)
• Fix bug in fingerprint bool->bitfield packing/unpacking code (#11)
• Fix integer overflow leading to incorrect allocations in similarity calculation code. (#20)
• Fix crash in most multithreaded APIs whenever exceptions are thrown inside of OpenMP loop. Exceptions now properly propagated to python (#18)

Miscellaneous

• Removed unsupported Bulk Similarity APIs (#12)

v0.1.0

Initial release of nvMolKit