State the scientific question

A reproducible chemistry AI workflow needs more than a saved model and a score. It needs enough context for another researcher to understand what was evaluated, reconstruct the procedure and judge whether the result supports the intended decision.

Start with a short evaluation brief: the target property or generation objective, relevant experimental conditions, intended use and criteria for a useful answer. Distinguish predicting a measured endpoint from optimizing a computational scoring function. Neither automatically establishes experimental usefulness.

Ask: Which decision will this evaluation inform? What inputs will be available at decision time? Which errors would make the output unsuitable? Define these points before selecting models or inspecting results.

Choose resources by evaluation role

The linked resource references describe different roles, not an already tested combined workflow:

  • RDKit provides molecular operations, descriptors and fingerprints. It can support a proposed preprocessing or feature-generation stage; record the operations actually selected.
  • Chemprop documents training, evaluation and prediction with message-passing neural networks, including molecule and reaction workflows. Select the relevant tutorial rather than assuming one input schema covers every task.
  • GuacaMol provides distribution-learning and goal-directed molecular-generation benchmarks, with standardized ChEMBL-derived datasets. It is an evaluation framework, not a generator itself.
  • MOSES combines molecular-generation reference data, baselines and collection-level metrics, including validity, novelty and diversity.
  • Matbench supplies 13 curated materials-science prediction tasks. Its task definitions must be examined before planning an evaluation.

Choose a resource because its task and data match your question. The list does not establish compatibility between packages or performance on your data.

Pin the environment and configuration

Create a run manifest before execution. Record the code revision, dependency versions, model configuration, checkpoint identifier, random seeds and hardware where relevant. Preserve configuration files alongside the results rather than relying on remembered defaults.

Version selection matters particularly for Chemprop: its prepared reference describes a substantial v2 rewrite, changed defaults and discontinued v1 support. Record the actual version used; do not assume workflows from different releases are interchangeable.

Separate environment reconstruction from numerical repeatability. Saving dependencies may help recreate a run without guaranteeing identical outputs. Plan repeated runs where appropriate, and record observed variation rather than claiming determinism without evidence.

Preserve inputs and transformations

Maintain an immutable source-data copy where permissions allow, plus a separate derived-data area. Record dataset origin, stable identifiers, checksums, target definitions, units and measurement conditions. Keep a transformation log linking each derived record to its source.

For every preprocessing step, document its purpose and consequences: molecular normalization, duplicate handling, missing-label exclusions, feature generation and unit conversion. Preserve the code and configuration responsible for these changes. If a record is rejected, retain its identifier and rejection reason where permitted.

Define the input/output contract explicitly. A proposed property-prediction workflow might accept molecular records with identifiers and measured targets, then produce predictions linked to those identifiers. Exact package schemas still require checking in the relevant documentation. For MOSES, the reference specifically describes generated SMILES as metric inputs and metrics.csv as an output of its baseline workflow.

Design evaluation before selecting results

Save split assignments and explain why they represent the intended deployment setting. Ask whether related structures, repeated measurements or preprocessing decisions could leak information between training and evaluation data. Keep model selection and final assessment distinct, including transformations learned from data.

Choose baselines and metrics before comparing candidates. Record metric definitions, aggregation rules, exclusions and the evidence used to justify each comparison. Keep predictions, errors, failed runs and baseline results—not just the best score.

For generation tasks, document the requested and returned sample counts, scoring objectives, invalid outputs and repeated-run procedure. MOSES recommends at least 30,000 generated molecules and repeated experiments to estimate metric variance. GuacaMol provides different interfaces for distribution-learning and goal-directed assessment; record which evaluation was used. Do not interpret their scores as prospective validation of generated molecules.

Worked planning example: a hypothetical property study

Suppose a team wants to compare a molecular property predictor with a descriptor-based baseline for a measured endpoint. This is a hypothetical plan, not a completed experiment or tested integration.

  1. Define the decision. Specify whether the predictor is intended to prioritize compounds for follow-up measurement. Record the endpoint, units, conditions and acceptable error criteria before training.
  2. Freeze the inputs. Preserve the source table with record identifiers, molecular representations and labels. Create a documented derived table after agreed normalization and exclusions.
  3. Prepare the evaluation. Save split membership and a rationale reflecting the intended use. Keep the final assessment data separate from candidate selection.
  4. Specify candidate workflows. Propose RDKit descriptors for a baseline and a Chemprop model for comparison. Check their selected interfaces and data handling before attempting integration.
  5. Retain outputs. Save identifier-linked predictions, configuration, checkpoint references, baseline outputs, metrics and failure logs for each planned run.
  6. Make the decision. Compare results against the predefined criteria and examine important error cases. If missing conditions or data coverage prevent a meaningful conclusion, document that limitation rather than promoting the best score.

The resulting record should explain both how to rerun the comparison and why it addresses—or fails to address—the scientific question.

Attach evidence and acknowledge limits

Separate source-described capabilities from locally observed findings and proposed evaluation steps. Keep documentation links with the claims they support. If source checks are performed, record their dates separately from experiment dates; a link alone is not evidence of completed review.

Benchmarks have boundaries. MOSES uses a filtered chemical reference collection, so its comparisons do not describe unrestricted chemical space. Matbench task inputs, metrics and splits are not detailed in the reference excerpt; consult its documentation before designing a run. Reproducible computational results still require domain interpretation and do not establish biological activity or synthetic feasibility.

Maintain the record: actionable checklist

Assign an owner and define reevaluation triggers, including new data, changed dependencies, revised models or altered preprocessing. Preserve a recovery procedure with artifact locations and access requirements.

Before closing an evaluation, check:

  • Scientific question and decision criteria are explicit.
  • Inputs, transformations and split assignments are traceable.
  • Environment, configuration and checkpoints are recorded.
  • Outputs, exclusions, failures and baselines are retained.
  • Source claims and experimental findings are distinguished.
  • Limitations, ownership and reevaluation triggers are documented.

Continue with resource discovery and scientific tasks to identify relevant records and source links. This guide does not certify scientific performance or compatibility.