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Active learning turns reaction prediction into a closed-loop experiment program: define the decision, train a model on existing results, score untested reactions, run the most useful feasible batch, add the measurements, and retrain. The “most useful” experiment depends on whether you want to find high yield quickly, map uncertain chemistry, improve selectivity, or satisfy several objectives at once. A sound workflow therefore treats the reaction label, candidate space, uncertainty estimate, laboratory capacity, and validation split as one design problem.
What active learning does in a reaction campaign
In ordinary supervised learning, a fixed dataset is used to fit a model and make predictions. Active learning adds a decision layer: the model chooses which unlabeled examples should be measured next. In reaction development, an example is usually a substrate–condition combination, and the new label may be feasibility, isolated yield, regioselectivity, enantioselectivity, conversion, or a vector of several outcomes.
The loop is iterative rather than a one-time ranking:
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- Define the experimental decision and its outcome metric.
- Assemble and quality-check the reactions already measured.
- Fit a predictive surrogate and estimate its uncertainty.
- Apply an acquisition rule to the untested candidate pool.
- Filter and batch the recommendations for safety, materials, equipment, and throughput.
- Run the experiments, record what actually happened, and return the measurements to the model.
- Retrain, evaluate against the intended generalization target, and repeat until the campaign’s stopping rule is met.
This distinction matters because a model that identifies one excellent reaction may not learn a broadly transferable reaction landscape. Conversely, a model chosen to expose uncertainty may spend measurements in regions that are not immediately the best performers. Active learning is successful only when the selection rule matches the decision the chemist needs to make.
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Start by defining the decision and the label
Choose the decision before choosing an algorithm
Write down the action the next batch should support. Common goals include finding any condition that makes a transformation work, maximizing yield, improving regioselectivity, identifying a robust condition across a substrate series, or optimizing yield and selectivity together. “Predict reaction outcome” is too vague to determine what should be selected.
Specify the variables that may change—substrate identity, catalyst, ligand, solvent, base, temperature, time, concentration, equivalents, atmosphere, and work-up—and the variables that remain fixed. A candidate is not simply a molecule; it is a complete, executable experiment definition. If the campaign only varies ligands at a fixed temperature and solvent, the model should not silently score combinations outside that scope.
Use a label that is available at the right point in the workflow
Binary feasibility, such as successful versus unsuccessful conversion, is often the earliest reliable label. Yield and selectivity require a successful reaction plus an analytical measurement, and some selectivity assignments require structural characterization. In a C–H borylation campaign reported in Nature Computational Science in 2026, feasibility was used as the first-pass label because regioselectivity could only be annotated after successful borylation and characterization. This sequential dependency is practical: an experiment that cannot produce the product cannot yet provide a regioselectivity label.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsDefine the class boundary or numerical measurement explicitly. For a binary label, document what counts as success, the detection limit, and whether “no product detected,” decomposition, and an unperformed reaction are different states. For a continuous label, record units, assay method, replicate policy, and censoring. Do not turn an unrun experiment into a negative result.
Build a trustworthy starting dataset
Normalize identities and conditions
Standardize reaction and compound identifiers before fitting a model. The same substrate may appear under different names, salts, protecting-group annotations, or stereochemical representations. Conditions need a consistent vocabulary for catalysts, ligands, solvents, bases, temperatures, times, concentrations, and equivalents. Preserve the original record as well as the normalized representation so an unusual prediction can be traced back to the notebook or instrument file.
Keep negative outcomes and their scope
Negative data are essential for feasibility classification and for learning where a reaction fails. A negative result means that the product was not observed under the tested conditions; it does not prove that the substrate is intrinsically incapable of reacting. Literature datasets can under-report negatives because authors preferentially publish successful conditions. Prospective screening with a systematic plate can therefore have a different class balance and a different meaning for “failure” than a historical collection.
Represent missingness rather than hiding it
Missing yield, selectivity, or analytical fields should remain distinguishable from zero. Record why a value is missing: reaction not run, sample lost, product below detection, analysis inconclusive, or measurement not required. Condition coverage is another form of missingness. If a substrate was tested with one catalyst but not another, the untested combination belongs to the candidate pool, not the negative class.
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Prevent leakage from repeated chemistry
Duplicates, near-duplicates, and measurements from the same substrate series can make a random split look easier than the real deployment problem. Keep metadata that identifies chemical series, scaffolds, reaction plates, time periods, and batches. You will need these fields later to build a validation split that reflects whether the next campaign concerns familiar substrates or genuinely new chemistry.
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Choose a surrogate model and make uncertainty useful
The surrogate is the fast model used to score many untested candidates. It can be a tree ensemble, a neural network, a Gaussian-process model, or another method appropriate to the representation and data volume. Model complexity is secondary to reliable predictions, calibrated uncertainty, and a scoring cycle fast enough for the candidate pool.
Fast oracle for selection
In the prospective borylation workflow, an XGBoost ensemble using ECFP4 fingerprints acted as the active-learning oracle. An ensemble can estimate uncertainty from disagreement among its members while retaining fast scoring over a large molecular pool. This does not establish that XGBoost is generally best; it illustrates a useful separation between a lightweight selection model and later, more expensive benchmarking.
Structure-aware models after data expansion
When the dataset grows, compare models that encode molecular geometry or graph structure. In the same study, geometric graph neural networks were evaluated after data expansion. On a final scaffold split, the reported mean Matthews correlation coefficient for the GNNs was 0.43–0.50, compared with 0.35 ± 0.12 for the strongest condition-aware XGBoost model. The result is specific to that reaction family, representation, and split; it is not a guarantee that a GNN will outperform a tree ensemble on another transformation.
Check uncertainty quality, not only accuracy
An acquisition rule can be harmed by poorly calibrated uncertainty. Test whether predictions assigned high uncertainty are actually more error-prone, and whether ensemble disagreement changes sensibly across familiar and novel chemistry. Calibration curves, reliability diagrams, selective-risk plots, and error rates by uncertainty quantile are more informative than a single accuracy number. If uncertainty is not meaningful, uncertainty-driven selection may simply pick noisy or out-of-domain structures.
Match the acquisition rule to the campaign objective
An acquisition function converts predicted outcomes and uncertainty into a priority score. The methods below are related but do not select the same kind of experiment.
| Strategy | What it favors | Useful when | Main risk |
|---|---|---|---|
| Pure exploitation | Highest predicted outcome | You need a strong candidate quickly and trust the model near the current best region | It can repeatedly sample a narrow chemical neighborhood and miss better or different behavior |
| Uncertainty sampling | Predictions with high uncertainty | The priority is learning the decision boundary or reducing model ignorance | Uncertainty may reflect noise, infeasibility, or out-of-domain chemistry rather than useful information |
| Expected improvement | Predicted gain over the current best, weighted by the probability of achieving it | You want improvement while retaining a measured exploration component | It depends on a meaningful current-best baseline and predictive uncertainty |
| Upper confidence bound (UCB) | Predicted mean plus a tunable uncertainty bonus | You want an explicit exploration–exploitation control | The exploration weight can dominate or vanish if poorly tuned |
| Bayesian Active Learning by Disagreement (BALD) | Examples with high model disagreement or epistemic information | You want measurements that should most reduce uncertainty about the underlying function | Disagreement can be misleading when the ensemble is misspecified or data are noisy |
| EHVI-family methods | Expected improvement in a multi-objective Pareto frontier | Yield, selectivity, cost, stability, or other outcomes must be optimized together | Requires explicit objective scales, constraints, and a clear treatment of trade-offs |
Expected improvement combines predicted performance with the chance of exceeding the current best. UCB makes the trade-off visible through its uncertainty coefficient. BALD uses epistemic disagreement to favor informative measurements, whereas generic uncertainty sampling may also react to irreducible noise. In multi-objective campaigns, an EHVI-style rule can prioritize candidates that expand the estimated Pareto frontier rather than collapsing everything into an unexplained weighted average.
A step-by-step implementation workflow
1. Define the candidate space
Create a machine-readable table of all experiments that could realistically be run. Include substrate and reagent identities, allowed condition ranges, discrete choices, safety restrictions, material limits, and analytical requirements. Remove combinations that are chemically impossible, unavailable, or prohibited before scoring. Keep a stable identifier for each candidate so predictions can be joined to execution and results.
2. Establish a seed set
Begin with the historical and pilot reactions that are relevant to the planned transformation. If the seed set is narrowly concentrated, add a deliberately diverse subset that spans substrate classes and condition families. Diversity is not a substitute for evidence, but it reduces the chance that the initial model treats an unrepresented region as confidently favorable.
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3. Create the training view
Build features from the exact information available before the reaction is run. Fingerprints, molecular graphs, descriptors, and encoded conditions should not contain product-side information or post-reaction analysis. Attach label-quality flags and replicate information. Keep a frozen test set or a time-forward holdout that will not influence acquisition decisions.
4. Fit the surrogate and audit predictions
Train the model on the currently labeled set, then inspect class balance, calibration, error by substrate family, and uncertainty by domain. A high predicted score outside the chemistry represented by the training data should trigger an out-of-domain review, not automatic execution. Compare at least one simple baseline with the proposed model so that gains are attributable to the model rather than data leakage or a favorable split.
5. Score and filter the pool
Calculate the selected acquisition value for every untested candidate. Apply hard constraints after scoring or incorporate them into the acquisition function so infeasible experiments cannot win by having a high predicted outcome. Mark candidates that duplicate an existing experiment, require unavailable material, exceed a reagent limit, or cannot be analyzed within the campaign schedule.
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6. Select a batch that the laboratory can execute
Batch size should match liquid-handling capacity, instrument queue time, material availability, and the cost of waiting for results. A very small batch gives rapid feedback but may underuse equipment; a large batch improves throughput but delays the next model update and increases exposure to a flawed ranking. For each selected experiment, record the intended protocol, plate position, controls, replicates, and stopping or escalation criteria.
7. Run, measure, and log actual conditions
Capture deviations from the plan: weighed quantities, actual temperature profile, reaction time, atmosphere, concentration, work-up, instrument method, and analyst. Store raw analytical evidence alongside the derived label. If a result is ambiguous, use a separate indeterminate state or schedule a confirmation experiment rather than forcing a binary decision.
8. Retrain and decide whether to continue
Append the new outcomes, refit the model, and recompute diagnostics. Compare the new batch with the previous best, uncertainty reduction, coverage of chemical space, and performance on the deployment-style validation split. Stop when the campaign reaches its objective, the marginal value of another batch falls below its cost, or constraints make further experiments unjustified.
How many experiments are needed?
There is no universal number that trains a useful reaction model. The requirement depends on the number of reaction families, condition variables, label noise, candidate-pool size, class imbalance, and the generalization target. A model intended to rank close analogues can need far fewer labels than one expected to extrapolate to new scaffolds.
Use learning curves and prospective checkpoints rather than a fixed promise. Plot performance and calibration against the number of labeled reactions, and track whether newly selected experiments add genuinely new scaffolds or merely replicate known chemistry. If the curve has not stabilized on the intended split, the campaign is not ready to claim broad predictive performance even if random-split accuracy looks high.
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Active learning can reduce the number of measurements needed to reach a modeling objective by selecting informative reactions instead of sampling uniformly. A 2020 reaction-screening study reported retrospective evidence that uncertainty sampling could identify an informative subset, with random learning as a baseline. That finding supports testing the approach; it is not a universal percentage saving or a reason to skip prospective validation.
Validation that reflects real chemical use
Random splits answer an easier question
Randomly assigning reactions to train and test sets can place close analogues of the same substrate or scaffold on both sides. Such a split measures interpolation within the collected distribution. It is useful for debugging but may overstate performance when the next campaign introduces new chemistry.
Scaffold and series splits test extrapolation
Bemis–Murcko scaffold splits and chemical-series splits keep related structures together and test whether the model transfers to less familiar substrates. In the borylation study, geometric GNNs outperformed the strongest condition-aware XGBoost model on the more challenging Butina and Bemis–Murcko scaffold splits, while results were more comparable on random splits. Report the split definition, number of examples, class balance, and uncertainty metrics with every benchmark.
Evaluate the selection policy prospectively
Offline metrics alone cannot prove that an acquisition function improves a campaign. Compare the number of experiments required to find a target outcome, the quality of the best result discovered, the diversity of explored chemistry, and the reliability of measurements. Distinguish optimizing one reaction from building a model that will generalize to future substrates.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the 2026 C–H borylation case demonstrates
A prospective study in Nature Computational Science provides a concrete, domain-specific example. Its initial dataset contained 518 substrates. The team benchmarked ten acquisition functions and selected BALD for prospective screening. The model scored a pool of 22,253 drug-like aromatic compounds, then tested 50 previously unreported substrates across approximately 96 reaction configurations over three active-learning rounds.
The combined dataset reached 6,865 binary reaction outcomes across 568 substrates. The campaign added 42 previously unseen Bemis–Murcko scaffolds; its unique-scaffold count rose from 105 to 147, a 40% increase in that particular campaign. These figures show what a multi-round exploration program can achieve with high-throughput screening, LC–MS analysis, scale-up, and structural characterization. They are not default targets for another reaction family, laboratory, or budget.
The workflow also illustrates the relationship between label design and acquisition. Feasibility was selected first because regioselectivity could only be assigned after successful borylation. The active-learning oracle used ECFP4 fingerprints and an XGBoost ensemble, while geometric GNNs were assessed after the dataset grew. The study therefore supports a staged design: use a fast model to choose experiments, then evaluate richer structure-aware models when enough labels exist.
“Binary reaction feasibility prediction is itself a primary deliverable of practical value to medicinal chemists,” the study states, because regioselectivity annotations depend on successful borylation.
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The strongest lesson is not that BALD, GNNs, or a particular scaffold count will transfer unchanged. It is that the acquisition rule, label sequence, infrastructure, and validation split must be reported together.
Common failure modes and fixes
The model repeatedly selects similar high-scoring molecules
This is exploitation without a coverage control. Add diversity or scaffold constraints, reserve part of each batch for exploration, or use an acquisition rule with an explicit uncertainty term. Check whether the candidate pool itself is narrow before blaming the model.
Uncertainty selects unusable experiments
High uncertainty can mean out-of-domain chemistry, poor representation, noisy measurement, or simple lack of data. Apply feasibility filters, inspect nearest neighbors and feature ranges, and separate epistemic disagreement from known assay noise. An uncertain but impossible experiment has little decision value.
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Verify that failures are real measured outcomes rather than unrun or inconclusive reactions. Use class-weighted training or calibrated thresholding when appropriate, but retain the original prevalence and report precision, recall, and Matthews correlation coefficient rather than accuracy alone.
Random-split scores are excellent but new substrates fail
Rebuild evaluation with scaffold, series, or time-forward splits. Inspect performance by chemical family and uncertainty quantile. If the deployment domain is new scaffolds, make that split the primary benchmark instead of presenting it as a secondary stress test.
Results cannot be reproduced
Version the candidate pool, feature generator, model, acquisition parameters, random seeds, protocols, and analytical files. Record the exact model snapshot that produced each batch. Without this provenance, a later retraining run may silently change which experiments would have been selected.
A single score hides conflicting objectives
State the weighting or constraint explicitly, or use a multi-objective method such as an EHVI-family acquisition rule. Report the Pareto trade-off among yield, selectivity, cost, stability, and other outcomes instead of presenting an unexplained composite number.
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- Is the decision—feasibility, yield, selectivity, robustness, or a multi-objective target—written in measurable terms?
- Are variable conditions, fixed conditions, candidate limits, and safety constraints explicit?
- Are negative, missing, censored, and unrun outcomes represented differently?
- Can the model estimate uncertainty, and has that estimate been calibrated?
- Does the acquisition rule match the balance between discovery and immediate performance?
- Does the batch fit material, equipment, analytical, and scheduling capacity?
- Will actual conditions and raw measurements be returned to the model with provenance?
- Does validation use the split that matches the chemistry the model must face next?
- Are stopping criteria based on campaign value rather than an arbitrary number of rounds?
Bottom line
Use active learning when experiments are expensive enough that choosing the next measurement matters. Define the outcome and candidate space first, preserve the limits of every negative result, combine prediction with a tested uncertainty estimate, and select batches that the laboratory can execute and analyze. Exploitation, uncertainty sampling, Bayesian optimization, BALD, and multi-objective acquisition encode different decisions; none is universally best. The C–H borylation study shows the promise of prospective, scaffold-expanding exploration, while its domain-specific results also show why every campaign must validate its own model, acquisition rule, and measurement workflow.
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