mlxmolkit-rdkit 0.5.0

GPU-accelerated molecular toolkit on Apple Silicon: conformer generation (ETKDG + MMFF94) and Butina clustering via Metal/MLX

mlxmolkit — GPU-accelerated molecular toolkit on Apple Silicon

Port of nvMolKit (CUDA) to Apple Metal via MLX. Two pipelines:

1. Molecular Clustering — Morgan FP → Tanimoto similarity → Butina clustering
2. 3D Conformer Generation — DG (4D) → ETK (3D) → MMFF94 optimization

Installation

pip install mlxmolkit-rdkit

Requires macOS with Apple Silicon (M1/M2/M3/M4). RDKit is needed for molecular input:

conda install -c conda-forge rdkit
pip install mlxmolkit-rdkit

Quick Start

from mlxmolkit import generate_conformers_nk, butina_tanimoto_mlx

# 3D conformers: 100 molecules x 10 conformers each
result = generate_conformers_nk(
    smiles_list=["c1ccccc1", "CC(=O)O", "CC(=O)Oc1ccccc1C(=O)O"],
    n_confs_per_mol=10,
    run_mmff=True,
)

# Clustering: 150k+ molecules
import mlx.core as mx
result = butina_tanimoto_mlx(mx.array(fp_bytes), cutoff=0.4)

Features

• Conformer Generation — Drop-in replacement for RDKit's ETKDG (EmbedMolecules). Supports ETKDG, ETKDGv2, ETKDGv3, srETKDGv3, KDG, ETDG, and pure DG.
• MMFF94 Optimization — GPU-accelerated force field optimization (MMFFOptimizeMoleculesConfs). All 7 MMFF energy terms with fused Metal kernel. Full BFGS or L-BFGS in-kernel (zero CPU round-trips).
• Molecular Clustering — Butina clustering at 150k+ molecules with divide-and-conquer memory management.
• N x k Parallel — Generate k conformers for N molecules simultaneously. Constraints shared across conformers (conf_to_mol indirection, 50% memory savings).

Performance

Conformer Generation (1000 distinct SPICE molecules, Apple M3 Max)

Benchmark uses 1000 distinct drug-like molecules from SPICE-2.0.1 (see data/benchmark_1000_smiles.csv).

Scale	Pipeline	Time	Throughput
N=100 x k=50	DG only	5.6s	900 conf/s
N=100 x k=50	DG + ETKDGv2	6.6s	761 conf/s
N=100 x k=50	DG + ETK + MMFF	6.6s	758 conf/s

Conformer Memory Scaling (DG + ETK + MMFF, batch=500)

Conformers	Batch	GPU (BFGS)	GPU (L-BFGS)	Time	Throughput
1,000	1000	5.1 MB	2.9 MB	0.43s	2,342/s
2,000	500	2.6 MB	1.5 MB	1.43s	1,402/s
4,000	500	2.6 MB	1.5 MB	1.91s	2,094/s
10,000	500	2.6 MB	1.5 MB	4.82s	2,075/s

GPU memory stays constant regardless of total conformers thanks to divide-and-conquer batching.

Scale Tests (1000 distinct molecules, Apple M3 Max)

Scale	Pipeline	Time	Throughput
N=1000, k=10	DG only	11.5s	870 conf/s
N=1000, k=10	DG + ETKDGv2	14.9s	672 conf/s
N=1000, k=10	DG + ETK + MMFF	15.0s	664 conf/s

All stages on Metal (including MMFF94 — zero RDKit post-processing).

Batch Size Impact

Larger batches = fewer kernel launches = higher throughput. Auto-sizing (default) picks the largest batch that fits in free memory.

GPU Memory per Conformer

Atoms	DG (4D)	ETK (3D)	MMFF (BFGS)	MMFF (L-BFGS)
5	1.9 KB	1.4 KB	1.3 KB	1.4 KB
12	4.4 KB	3.3 KB	6.0 KB	3.3 KB
21	7.6 KB	5.7 KB	17.2 KB	5.7 KB
30	10.8 KB	8.1 KB	34.1 KB	8.1 KB
50	18.0 KB	13.5 KB	92.0 KB	13.5 KB
64	23.1 KB	17.3 KB	149.2 KB	17.3 KB

MMFF BFGS memory grows as O(n^2) due to the dense Hessian (n_atoms x 3)^2. BFGS is faster than L-BFGS at all typical drug-like sizes (up to 74 atoms with H) because the better curvature information requires fewer iterations. L-BFGS is only needed for very large molecules (>150 atoms) where the Hessian exceeds ~1 MB per conformer.

Molecule	Atoms (with H)	BFGS	L-BFGS	Winner
Methane	5	0.255s	0.215s	BFGS
Benzene	12	0.213s	0.222s	~tie
Aspirin	21	0.241s	0.230s	~tie
Testosterone	49	0.364s	0.335s	BFGS
Cholesterol	74	0.590s	0.486s	BFGS

Recommendation: use BFGS (default) for all molecules <150 atoms with H. The pipeline auto-switches to L-BFGS at 150+ atoms (mmff_use_lbfgs=None, the default).

Important: Always add explicit hydrogens (Chem.AddHs) before conformer generation. Convergence is significantly better with explicit H because the distance geometry constraints are more complete and the force field terms (bond/angle/torsion) are fully defined. The pipeline calls AddHs automatically.

With 64 GB unified memory, a single batch can hold:

Molecule size	DG/ETK	MMFF (BFGS)	MMFF (L-BFGS)
12 atoms	~9.8M conformers	~7.4M	~9.8M
30 atoms	~3.9M conformers	~1.3M	~3.9M
64 atoms	~1.8M conformers	~300K	~1.8M

The divide-and-conquer queue automatically splits into multiple batches when total exceeds free memory.

Clustering (Enamine REAL subset, Apple M3 Max)

N	Fused sim→CSR	Butina	Total	vs RDKit	Memory
20k	0.26s	0.09s	0.35s	152x	0.1 MB
50k	1.26s	0.36s	1.62s	—	0.5 MB
100k	4.87s	0.97s	5.84s	—	1.3 MB
150k+	blockwise	—	scales	—	bounded

ETKDG Variant Comparison (N=20, k=50, same molecule)

Throughput for homogeneous batches (all conformers of the same molecule, best case):

Variant	conf/s	Convergence
DG	7,549	96.6%
KDG	7,243	96.6%
ETDG	1,844	96.6%
ETKDG	6,064	96.6%
ETKDGv2	6,228	96.6%
ETKDGv3	6,636	96.6%
srETKDGv3	6,678	96.6%

Note: throughput is lower for diverse molecule batches due to variable atom counts and padding overhead. The Scale Tests section above shows realistic numbers for heterogeneous batches.

Architecture

Conformer Generation (N x k parallel)

SMILES x N
    |
[RDKit CPU] Extract params ONCE per molecule
    |
[Pack] SharedConstraintBatch (conf_to_mol indirection)
    |
+-- Stage 1: DG minimize (4D, Metal TPM=32) --------+
|   One threadgroup per conformer                    |
|   L-BFGS in-kernel, GPU-parallel line search       |
|   Shared constraints via conf_to_mol               |
+----------------------------------------------------+
    |
[Extract 3D] Drop 4th coordinate
    |
+-- Stage 2: ETK minimize (3D, Metal TPM=32) --------+
|   CSD torsion + improper + 1-4 distance             |
|   Optional parallel_grad for large molecules        |
+-----------------------------------------------------+
    |
+-- Stage 3: MMFF94 optimize (Metal, in-kernel) ------+
|   7 energy terms: bond, angle, stretch-bend,         |
|   OOP, torsion, vdW, electrostatic                   |
|   BFGS (default) or L-BFGS option                    |
+------------------------------------------------------+
    |
Optimized 3D conformers

Clustering (divide-and-conquer for 150k+)

Morgan FP (RDKit CPU)
        |
   uint8 -> uint32 packing
        |
+-- N <= 100k: Fused Metal Kernel ------+
|   Single dispatch, no NxN matrix       |
+-- N > 100k: Blockwise D&C ------------+
|   Tile both dimensions (auto-sized)    |
|   mx.eval() between tiles (free GPU)  |
+----------------------------------------+
        |
   Butina greedy (CPU, numpy CSR)
        |
   Clusters

Adaptive Iteration Scaling

Iterations auto-scale by molecule complexity (default). Small molecules converge early via in-kernel TOLX/gradient checks — no wasted GPU compute.

Formula: max_iters = base + scale * max(n_atoms, sqrt(n_constraints))

Molecule	Atoms	Constraints	DG iters	ETK iters	MMFF iters
Methane	5	10	400	200	275
Benzene	12	66	540	270	380
Aspirin	21	210	720	360	515
Testosterone	49	1176	1280	640	935
64-atom	64	2016	1580	790	1160

Override with explicit values when needed:

# Auto (default) — scales with molecule size
result = generate_conformers_nk(smiles_list, n_confs_per_mol=10)

# Fixed iterations for fine control
result = generate_conformers_nk(smiles_list, n_confs_per_mol=10,
    dg_max_iters=1000, etk_max_iters=500, mmff_max_iters=400)

Optimization Options

Option	Flag	Effect	When to use
Auto iterations (default)	dg_max_iters=0	Scales with molecule size	Always (default)
Warm-start retry	automatic	Re-runs non-converged with 2x iters	Always (automatic)
ETK parallel gradient	parallel_grad=True	1.18x ETK speedup	Many distance constraints
DG parallel gradient	parallel_grad=True	Parallelizes dist gradient	>500 distance constraints
MMFF94s variant	mmff_variant="MMFF94s"	Softer torsion barriers	Conjugated/aromatic molecules
MMFF L-BFGS	mmff_use_lbfgs=True	5x less memory	Molecules >50 atoms
MMFF BFGS (default)	mmff_use_lbfgs=False	2x faster for small mols	Molecules <50 atoms
ETKDG variant	variant="ETKDGv3"	7 variants supported	Choose per use case

MMFF94 Force Field Variants

Variant	Flag	Torsion barriers	Best for
MMFF94 (default)	mmff_variant="MMFF94"	Standard	General molecules
MMFF94s	mmff_variant="MMFF94s"	Softer for conjugated systems	Aromatic, planar, conjugated

Usage

3D Conformer Generation

from mlxmolkit.conformer_pipeline_v2 import generate_conformers_nk

# Basic: 10 conformers per molecule, ETKDGv2
result = generate_conformers_nk(
    smiles_list=["c1ccccc1", "CC(=O)O", "CC(=O)Oc1ccccc1C(=O)O"],
    n_confs_per_mol=10,
)
for mol in result.molecules:
    print(f"{mol.n_atoms} atoms, {len(mol.positions_3d)} conformers")

# Full pipeline with MMFF94
result = generate_conformers_nk(
    smiles_list=["c1ccccc1", "CC(=O)O"],
    n_confs_per_mol=50,
    variant="ETKDGv3",
    run_mmff=True,
    mmff_use_lbfgs=False,       # BFGS (default, fast for <50 atoms)
    max_confs_per_batch=500,    # divide-and-conquer batch size
)

# Pure distance geometry (no torsion refinement)
result = generate_conformers_nk(
    smiles_list=["c1ccccc1"],
    n_confs_per_mol=100,
    variant="DG",
)

# Large molecules: use L-BFGS for MMFF
result = generate_conformers_nk(
    smiles_list=[large_smiles],
    n_confs_per_mol=20,
    run_mmff=True,
    mmff_use_lbfgs=True,        # L-BFGS for >50 atoms
)

Example Script

# Basic: 20 molecules x 10 conformers
python examples/conf3d_example.py

# Scale test: 1000 molecules x 10 conformers with MMFF
python examples/conf3d_example.py --n-mols 1000 --n-confs 10 --mmff

# All options
python examples/conf3d_example.py --n-mols 100 --n-confs 20 --variant ETKDGv3 \
    --mmff --mmff-variant MMFF94s --batch-size 200

# Custom SMILES
python examples/conf3d_example.py --smiles "c1ccccc1" "CC(=O)O" --n-confs 50 --mmff

Molecular Clustering

from mlxmolkit import butina_tanimoto_mlx
import mlx.core as mx

# Automatic: fused kernel for N<=100k, blockwise for N>100k
result = butina_tanimoto_mlx(mx.array(fp_bytes), cutoff=0.4)
print(f"{len(result.clusters)} clusters")

Low-level API

from mlxmolkit import (
    fp_uint8_to_uint32,
    fused_neighbor_list_metal,
    tanimoto_neighbors_blockwise,
    butina_from_neighbor_list_csr,
)

fp_u32 = fp_uint8_to_uint32(mx.array(fp_bytes))

# Small N: fused single-dispatch
offsets, indices = fused_neighbor_list_metal(fp_u32, cutoff=0.4)

# Large N (150k+): divide-and-conquer blockwise
offsets, indices = tanimoto_neighbors_blockwise(fp_u32, cutoff=0.4)

result = butina_from_neighbor_list_csr(offsets, indices, N, cutoff=0.4)

Tests

pip install -e .
pytest tests/ -v

Conformer Quality vs RDKit

mlxmolkit conformers rescored by RDKit's MMFF94 for fair comparison (k=20, ETKDGv2):

After MMFF optimization, mlxmolkit conformers converge to the same energy basins as RDKit:

Molecule	Atoms	RMSD (pre-MMFF)	RMSD (post-MMFF)	E gap
Benzene	12	0.12 A	0.00 A	0.0
Aspirin	21	0.98 A	0.00 A	0.0
Ibuprofen	33	1.79 A	0.96 A	0.6
Acetaminophen	20	0.99 A	0.00 A	0.0

Full nvMolKit pipeline: DG (4D) → 4D→3D collapse → setReferenceValues → stereo checks → ETK (3D) → MMFF94. Bond/angle geometry matches RDKit within 0.03 A. After MMFF, energy gap is <1 kcal/mol.

References

• nvMolKit — NVIDIA's CUDA implementation (Apache 2.0)
• shivampatel10/mlxmolkit — TPM threadgroup kernels and MMFF Metal implementation
• RDKit blog: Butina clustering with nvMolKit
• MLX — Apple's ML framework with Metal kernel support
• MMFF94 — Halgren, J. Comput. Chem. 1996
• Butina, D. (1999) — Performance of Kier-Hall and molecular connectivity indices