molhub gives every molecular dataset the same interface — index by position, iterate sample by sample, or slice with subsets — and ships uploaders for publishing your own data to HuggingFace Hub and Figshare. Samples carry atomic coordinates, forces, and computed properties in a consistent structure, so downstream code works identically across QM9, revMD17, 3BPA, or your own CSV files.

Under active development. Public APIs may change between minor releases.

Capabilities

Module	Capability
molhub.dataset (protocols)	MapDataset and IterableDataset runtime-checkable protocols, plus TargetSchema declaring graph-level vs atom-level targets
molhub.dataset (helpers)	InMemoryDataset wraps a list of frames; SubsetDataset slices any map-style dataset by index
QM9Source	130,831 small organic molecules with quantum-chemical properties — auto-downloads & caches from Figshare
RevMD17Source	Revised MD17 molecular-dynamics trajectories for 10 molecules, with energies and forces
ThreeBPASource	3BPA temperature-transferability benchmark — extended-XYZ files at 300 K / 600 K / 1200 K
CSVDataset	Generic CSV loader for local files or remote URLs — zero extra dependencies, each row becomes a Frame
HuggingFaceUploader	Upload files, folders, and datasets to a HuggingFace Hub repository
FigshareUploader	Create Figshare articles and upload files via the Figshare REST API

Each sample is a molpy Frame: the atoms block holds per-atom data (element, x, y, z, number, and optionally fx, fy, fz), while graph-level targets such as energy live in frame.metadata.

Install

pip install molhub                 # core (datasets + protocols + Figshare uploader)
pip install molhub[huggingface]    # + HuggingFace Hub upload support
pip install molhub[dev]            # + dev tooling (pytest, pytest-cov, pytest-mock, ruff)

Requires Python >= 3.10. Core dependencies: molcrafts-molpy >= 0.3.0, tqdm, requests >= 2.28.

Quick start

from molhub.dataset import QM9Source, CSVDataset

# Load QM9 — auto-downloads & caches the 130k-molecule tarball
qm9 = QM9Source("./data/qm9")
print(len(qm9))          # 130831
frame = qm9[42]          # one sample carries atoms + computed properties
print(frame.metadata)    # {'A': ..., 'B': ..., 'U0': ..., ...}

# Load any CSV from a URL or local file
ds = CSVDataset("https://zenodo.org/records/14980914/files/LAMALAB_CURATED_Tg_structured.csv")
print(ds.headers)        # ['labels.SMILES', 'labels.Exp_Tg(K)', ...]
frame = ds[0]
print(frame.metadata["labels.Exp_Tg(K)"])  # 373.0

Dataset protocols

Every dataset conforms to one of two runtime-checkable protocols, so downstream code can consume them generically.

MapDataset — index-addressable; supports len() and random access by integer position. Use when samples fit in memory or are backed by a random-access store.

from molhub.dataset import MapDataset

class MyDataset:
    def __len__(self) -> int: ...
    def __getitem__(self, idx: int) -> Frame: ...
    @property
    def source_id(self) -> str: ...

assert isinstance(MyDataset(), MapDataset)  # runtime-checkable protocol

IterableDataset — streaming; samples are yielded one at a time. Use for lazy file readers, on-the-fly generation, or datasets too large to hold in memory.

from molhub.dataset import IterableDataset

class MyStream:
    def __iter__(self) -> Iterator[Frame]: ...
    @property
    def source_id(self) -> str: ...

assert isinstance(MyStream(), IterableDataset)

TargetSchema — each dataset declares where its targets live, so batching and collation logic knows what to expect:

Target level	Location	Example
graph_level	frame.metadata	energy, homo, lumo
atom_level	atoms block columns	fx, fy, fz

InMemoryDataset / SubsetDataset — convenience helpers for wrapping and slicing:

from molhub.dataset import InMemoryDataset, SubsetDataset

inmem = InMemoryDataset(frames, name="my-frames")        # wrap a list
subset = SubsetDataset(qm9, indices=[0, 10, 100, 1000])  # slice by index

Built-in datasets

QM9 (QM9Source) — 130,831 small organic molecules with quantum-chemical properties. Reference: Ramakrishnan et al., Scientific Data 1, 140022 (2014).

from molhub.dataset import QM9Source

source = QM9Source("./data/qm9", targets=["U0", "H", "gap"])
# Auto-downloads from Figshare, caches locally, lazy-loads on first access

Graph-level targets: A, B, C, mu, alpha, homo, lumo, gap, r2, zpve, U0, U, H, G, Cv.

revMD17 (RevMD17Source) — revised MD17 molecular-dynamics trajectories (10 molecules). Reference: Christensen & von Lilienfeld, MLST (2020).

from molhub.dataset import RevMD17Source

source = RevMD17Source("./data/revmd17", molecule="aspirin")
frame = source[100]
# atoms block: element, x, y, z, number, fx, fy, fz
# metadata: {'energy': ...}

Available molecules: aspirin, azobenzene, benzene, ethanol, malonaldehyde, naphthalene, paracetamol, salicylic, toluene, uracil.

3BPA (ThreeBPASource) — temperature-transferability benchmark at 300 K, 600 K, and 1200 K. Reference: Kovacs et al., J. Chem. Theory Comput. (2021).

from molhub.dataset import ThreeBPASource

train = ThreeBPASource("./data/3bpa/train_300K.xyz", tag="train_300K")
test_600 = ThreeBPASource("./data/3bpa/test_600K.xyz", tag="test_600K")

CSVDataset — generic CSV loader, works on local files and remote URLs. No extra dependencies.

from molhub.dataset import CSVDataset

ds = CSVDataset("/path/to/data.csv")
ds = CSVDataset("https://example.com/data.csv")
# cached to $MOLHUB_CACHE_DIR, falling back to ~/.cache/molhub

Uploaders

HuggingFace Hub — requires pip install molhub[huggingface].

from molhub.uploader import HuggingFaceUploader

uploader = HuggingFaceUploader(token="hf_...")  # or set HF_TOKEN

uploader.upload_file("data/qm9.tar.bz2", repo_id="my-org/qm9", path_in_repo="raw/qm9.tar.bz2")
uploader.upload_folder("data/processed/", repo_id="my-org/dataset", path_in_repo="processed/")
uploader.upload_dataset("data/dataset.h5", repo_id="my-org/new-dataset")  # create + upload

Figshare — uses the core requests dependency; no extra install needed.

from molhub.uploader import FigshareUploader

uploader = FigshareUploader(token="...")  # or set FIGSHARE_TOKEN

result = uploader.upload_dataset(
    "data/qm9.tar.bz2",
    title="QM9 dataset",
    description="Raw QM9 molecular properties dataset",
    tags=["quantum-chemistry", "molecules"],
)

MolCrafts ecosystem

Project	Role
molpy	Python toolkit — the shared molecular data model & workflow layer
molrs	Rust core — molecular data structures & compute kernels (native + WASM)
molpack	Packmol-grade molecular packing (Rust + Python)
molvis	WebGL molecular visualization & editing
molexp	Workflow & experiment-management platform
molnex	Molecular machine-learning framework
molq	Unified job queue — local / SLURM / PBS / LSF
molcfg	Layered configuration library
mollog	Structured logging, stdlib-compatible
molhub	Molecular dataset hub — this repo
molmcp	MCP server for the ecosystem
molrec	Atomistic record specification

License

BSD-3-Clause — see LICENSE.

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