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Molecular generation and PyTDC oracles

This reference targets PyTDC 1.1.15, verified 2026-07-23. Oracle names and behavior are heterogeneous. Discover the installed registry and classify side effects before constructing an Oracle.

PyTDC's role

Core PyTDC provides:

  • molecular corpora through tdc.generation.MolGen;
  • Evaluator functions for generated sets;
  • scalar, composite, checkpoint-backed, remote-service, and docking oracles.

It does not supply one universal trainable molecule generator. Users bring or implement the generative model and must define a scientifically justified objective, constraints, validation protocol, and experimental follow-up.

Discover names without calling an oracle

uv run --python 3.11 --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \
  python scripts/discover_metadata.py --kind oracles --limit 100

This reads tdc.metadata.oracle_names; it does not instantiate an oracle, download a checkpoint/receptor, or transmit a SMILES string.

Use exact names. PyTDC fuzzy matching can silently normalize approximate input, which is undesirable for expensive or remote operations.

Side-effect categories in the stable metadata

Local scalar property

Verified direct local scalar name:

qed

qed requires RDKit but no PyTDC model artifact. It is the quantitative estimate of drug-likeness; higher is more drug-like on its documented 0–1 scale.

Although upstream metadata groups logp and sa with “trivial” oracles, source and execution verification show that both call calculateScore, which downloads the fpscores artifact when absent. Treat both as download-backed.

Local composite/GuacaMol-style objectives

The registry contains rediscovery, similarity, isomer, median, MPO, SMARTS, and hop objectives. Some names use fixed targets; *_meta variants require constructor arguments such as target_smiles.

Do not infer a constructor signature or score direction from the name. Read the matching official oracle section and stable source before use. The bundled CLI does not execute these objectives.

Checkpoint-backed models

Stable download metadata includes:

drd2, gsk3b, jnk3, cyp3a4_veith, fpscores,
drd2_current, gsk3b_current, jnk3_current

Constructing one can call Harvard Dataverse and write a model file beneath ./oracle. For DRD2/GSK3B/JNK3, PyTDC may normalize the request to a _current checkpoint according to the installed scikit-learn version.

Checkpoint files are serialized model artifacts. Review source, origin, local path, size, and trust boundary before download/loading. The bundled CLI supports only bounded LogP/SA/DRD2/GSK3B/JNK3/CYP3A4_Veith calls and requires both --execute and --download.

The 1.1.15 LogP oracle is not raw octanol/water partition alone. It implements the normalized penalized logP objective: RDKit MolLogP plus a normalized negative synthetic-accessibility term and a large-cycle penalty. Higher is the objective's optimization direction. SA returns synthetic accessibility, for which lower conventionally means easier synthesis. Do not combine either with other scores without documenting transformation, scale, and direction.

Distribution evaluators

The Oracle/Evaluator registries include:

novelty, diversity, uniqueness, validity, fcd_distance, kl_divergence

These operate on collections, and several need a training/reference set. They are not interchangeable scalar objectives:

  • validity/uniqueness/novelty/diversity are higher by their documented definitions;
  • FCD distance and KL divergence are lower as distance/divergence quantities;
  • novelty and distribution comparisons depend on the exact reference corpus and canonicalization;
  • optional chemical-model dependencies may be substantial.

Use Evaluator and the official input signature. Do not send these through the bundled scalar-scoring helper.

Remote synthesis services

Metadata includes askcos and ibm_rxn. Official documentation describes extra host/API inputs. Calling them can transmit molecular structures and credentials to an external service.

Before any call:

  1. identify the exact service operator and current terms;
  2. determine whether the molecule is confidential or patent-sensitive;
  3. obtain explicit user approval for transmission and cost;
  4. read only the named credential required by that service;
  5. never print or save the credential in JSON, logs, or command arguments;
  6. enforce request/time/call limits.

The bundled script intentionally refuses these remote services. The 1.1.15 docs may show historical endpoints or token flows; verify them with the service provider.

Receptor and docking oracles

The registry contains PDB-specific names ending in _docking, _docking_normalize, and _docking_vina, plus specialized names such as pyscreener, docking_score, smina, rmsd, and kabsch_rmsd.

These paths can involve:

  • receptor PDB/PDBQT downloads;
  • local executables and substantial CPU/storage;
  • user-specified box centers/sizes;
  • generated conformers and temporary files;
  • license restrictions for docking software;
  • remote or proprietary synthesis scoring in benchmark evaluation.

Raw docking energies and normalized variants have different directions. Never infer direction from a generic “Docking” label. The bundled molecular CLI and benchmark CLI do not execute docking.

Bounded local scoring

Plan first:

python scripts/molecular_generation.py score \
  --oracle QED \
  --smiles "CCO"

The JSON plan reports classification, input count, runtime directory, and required acknowledgement. It does not instantiate Oracle.

Execute a local scalar only after review:

python scripts/molecular_generation.py score \
  --oracle QED \
  --smiles "CCO" \
  --execute

Execute a supported checkpoint-backed model only after approving the checkpoint:

python scripts/molecular_generation.py score \
  --oracle DRD2 \
  --smiles "CCO" \
  --runtime-dir .pytdc-oracles \
  --execute --download

The helper:

  • accepts at most 500 SMILES and a 1 MiB input file;
  • keeps output in input order;
  • truncates long strings;
  • never ranks candidates or assumes score direction;
  • changes into the safe runtime directory so upstream ./oracle writes remain contained;
  • refuses remote services, docking, distribution metrics, and composite objectives.

Direct Oracle API

After side-effect review:

from tdc import Oracle

oracle = Oracle(name="QED", num_max_call=100)
scores = oracle(["CCO", "c1ccccc1"])

num_max_call bounds accumulated valid scalar calls for supported paths. It is not a network timeout, memory limit, or cost limit.

For list input, PyTDC validates each SMILES with RDKit. Invalid entries can receive the oracle's default value (commonly zero) rather than raising. Pre-validate structures, preserve an explicit validity flag, and do not interpret the default as a measured low score.

Oracle results are predictions or computed proxies, not experimental evidence. Applicability domains, model training data, stereochemistry, protonation, tautomerization, salts, and assay context can materially change interpretation.

MolGen datasets

Discover the exact stable registry:

python scripts/discover_metadata.py --kind datasets --task MolGen

Plan a random split:

python scripts/molecular_generation.py dataset \
  --dataset MOSES \
  --seed 42 \
  --data-dir .pytdc-molgen

MolGen corpora can contain hundreds of thousands or millions of structures. Review the official page, per-dataset license, compressed/decompressed size, free disk, and network budget. Execution intentionally requires both flags:

python scripts/molecular_generation.py dataset \
  --dataset MOSES \
  --seed 42 \
  --data-dir .pytdc-molgen \
  --execute --download

PyTDC 1.1.15's MolGen loader exposes random split only. The supplied seed controls test sampling, while the generic splitter uses fixed random_state=1 for validation sampling.

Goal-directed optimization safeguards

Before optimizing:

  • define whether every objective is maximized, minimized, targeted, or constrained;
  • normalize only with justified transformations;
  • separate train, validation, and final evaluation budgets;
  • cap total unique oracle calls and deduplicate canonical structures;
  • record invalid/failed/time-out results rather than silently dropping them;
  • retain all candidates and scores needed for audit, but keep chat/CLI output bounded;
  • monitor exploitation of model artifacts and out-of-domain structures;
  • evaluate novelty against the exact declared training/reference set;
  • add medicinal-chemistry, synthesizability, selectivity, safety, and diversity review rather than relying on a single score;
  • treat computational hits as hypotheses requiring expert and experimental validation.

Do not claim a weighted sum is scientifically valid merely because every term is numerical.

Unsupported historical examples removed

The stable 1.1.15 metadata/public imports do not support old examples that presented the following as generic ready-to-use APIs:

  • PairMolGen / Prodrug;
  • MolGen(name="GuacaMol");
  • evaluate_guacamol(...);
  • scalar MW, Lipinski, generic Docking, or generic Vina oracle names;
  • target oracles such as 5HT2A, ACE, MAPK, CDK, P38, PARP1, or PIK3CA.

Do not restore these names without verifying a newer official package registry and source implementation.