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)