REINVENT returns SMILES in results.csv inside the results zip, not the callback — and in a sampling run the score is negative log-likelihood, not quality.

REINVENT returns generated molecules as SMILES plus run metrics. Export the candidates you keep and feed them into downstream tools — ADMET-AI for property screening, DiffDock for a pose hypothesis, and AiZynthFinder to check synthetic feasibility before you commit to the next round.

Read the score before you rank on it

The platform's default run type is sampling, which samples the prior with no scoring function. The number carried in that mode is the negative log-likelihood of the SMILES under the prior — how typical the molecule is of the training set, not how active, drug-like, or makeable it is (lower NLL means more typical). Per-component scores exist only in scoring, sampling_scoring and staged_learning runs, and are then whatever you configured in scoring functions. Nothing in a result has been measured. See how REINVENT results compare to the bench.

The callback payload does not carry the molecules: generated_molecules is always null by design to keep the payload bounded, and the full list lives in results.csv inside the results zip at the returned S3 URI.

Generated Molecules and Monitoring

REINVENT 4 produces molecules through its RNN or transformer-based generators during reinforcement learning optimization. Proper logging and export of generated molecules is essential for downstream analysis and experimental validation.

For output format specifications, see the REINVENT 4 documentation.

What to log

Mean/median total score per step
Validity, uniqueness, and novelty percentages
Top-N SMILES with per-component scores
Checkpoint paths for prior/agent after each epoch

Exporting molecules

import pandas as pd
from reinvent_scoring import CustomSumScorer

# assume `top_smiles` is a list of (smiles, score)
df = pd.DataFrame(top_smiles, columns=["smiles", "score"])
df.to_csv("/workspace/outputs/top.csv", index=False)

For SDF exports, generate 3D with RDKit first, then write via SDWriter.

Checkpoints

Save agent checkpoints periodically (e.g., every epoch) to /workspace/outputs/checkpoints/epoch_X.ckpt.
Record the random seed and scoring config used for each checkpoint in metadata.json.
Keep the best-performing checkpoint (highest mean score) separately tagged.

Alerting on drift

Watch for drops in validity or uniqueness; often caused by overly strict filters or high learning rates.
If novelty falls, adjust diversity filters or reduce curriculum difficulty.

Ticket checklist

Config file, seed, and scoring parameters
Paths to checkpoints and top.csv
Plots or CSV excerpts of score/validity over time

Output analysis for different design modes

REINVENT 4 supports multiple molecular design tasks, each producing distinct output characteristics:

Design Mode	Primary Output	Key Metrics
De novo design	Novel molecular structures	Validity, uniqueness, novelty
Scaffold hopping	New scaffolds carrying the pharmacophore you scored for	Scaffold diversity, predicted activity from your own scoring model
R-group replacement	Varied substituents	Change in predicted properties
Linker design	Fragment connectors	Linker length; makeability is unmeasured, check it with AiZynthFinder

Integration with downstream tools

Generated molecules can be evaluated using complementary platform tools:

ADMET-AI: predict pharmacokinetic properties of generated compounds
DiffDock: propose a binding pose. It returns no affinity, and neither does the platform's MM-GBSA
AiZynthFinder: plan a retrosynthetic route for top candidates — the only makeability check available here
DeepSol: predict aqueous solubility for formulation planning

Canvas Visualization

When a REINVENT job completes, a result card appears on the canvas.

Visualization: 2D molecule grid with score distribution and property histograms

Tier 2 Metrics

Molecules generated count
Best score — in a sampling run this is the lowest NLL, not a normalized 0-1 quality score
Diversity index
Unique scaffold count

Generator Types

Type	Description	Use Case
reinvent	De novo from scratch	Novel chemical space exploration
libinvent	R-group decoration on scaffold	SAR around a known scaffold
linkinvent	Linker design between fragments	PROTAC and bifunctional molecule design
mol2mol	Molecule-to-molecule optimization	Improving a lead compound
pepinvent	Peptide design	Therapeutic peptide generation

Outputs & Monitoring