Active learning prioritizes the next reaction by using the results of earlier experiments to decide which untested condition or substrate will be most useful to run next. The practical loop is: define the decision and outcome, assemble consistent starting data, fit a predictive model with calibrated uncertainty, score the candidate pool with an acquisition rule, run a feasible batch, record the measured results, and retrain. Repeat until the campaign has enough evidence for its chemical decision.
The best selection rule depends on whether you are trying to find a high-performing reaction quickly, map uncertain chemistry, build a model that generalizes to new scaffolds, or optimize several outcomes at once. Active learning is therefore an experimental-design method, not simply a model that ranks reactions once.
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What active learning changes in a reaction campaign
In ordinary screening, a fixed library of reactions is selected in advance and tested regardless of what the early results reveal. Active learning makes the campaign adaptive. Every measured outcome changes the model, and the updated model changes the next batch.
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A candidate can be a substrate, a complete reaction condition, or a substrate–condition pair. The outcome might be binary feasibility, isolated yield, regioselectivity, enantioselectivity, conversion, or a weighted combination. The candidate representation and the label must match that decision. A model trained to predict feasibility cannot, by itself, answer which condition gives the highest isolated yield.
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The loop has two competing uses for an experiment:
- Exploitation: test candidates the model already believes are good.
- Exploration: test candidates where the model is uncertain or where the result would improve its understanding of chemical space.
Neither objective is universally correct. A medicinal-chemistry campaign seeking one workable route may favor rapid discovery of feasible reactions. A method-development campaign or a model intended for new chemical series may need more exploration, even when those experiments are not immediate winners.
Start by defining the decision and the candidate space
Choose the outcome before choosing the algorithm
Write down the decision the campaign must support. Typical first-pass choices are:
- Can the reaction occur under the tested conditions?
- Which conditions maximize yield?
- Which conditions deliver the desired regio- or stereoselectivity?
- Which candidates satisfy several constraints, such as yield above a threshold while minimizing catalyst loading?
Use a label that is available reliably at the stage where decisions are made. In a 2026 prospective C–H borylation study, feasibility was selected as the first-pass label because regioselectivity could be annotated only after successful borylation and structural characterization. Treating feasibility as a binary deliverable avoided asking the model to predict a downstream measurement that did not exist for failed reactions.
Specify what can vary
Define the candidate space explicitly. Depending on the project, variables may include substrate identity, catalyst, ligand, base, solvent, temperature, reaction time, concentration, equivalents, atmosphere, and work-up. State which variables are fixed and which are allowed to change. A candidate pool that silently mixes incompatible scales, work-up procedures, or analytical methods will produce labels that are difficult to compare.
For each proposed experiment, retain the complete intended condition record. Do not reduce a reaction to a substrate name and a single score if the downstream model needs catalyst, solvent, temperature, or stoichiometry to generalize.
Build a trustworthy starting dataset
Normalize identities and conditions
Before fitting a model, make reaction identities and condition fields consistent. Standardize structure representations, resolve duplicate compounds, normalize units, and distinguish an unknown value from a value that was not applicable. Record how an outcome was measured and whether it is qualitative or quantitative.
Keep the provenance of each result. A yield from an isolated product, an LC–MS area percentage, and a conversion estimate are not interchangeable labels. If the campaign changes analytical procedures, mark the change so the model does not mistake an instrument or protocol effect for chemistry.
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A failed reaction is valuable training data, but it is negative only under the conditions actually tested. A literature report that contains no successful example for a substrate does not prove that the substrate is intrinsically unreactive; the reported condition set may simply be narrow. Preserve the tested conditions alongside every negative outcome.
Class imbalance is common when feasible reactions are rare. Report the number of positive and negative outcomes, use metrics suited to that balance, and avoid judging a model by accuracy alone. Missing condition coverage and selective reporting can also bias the initial model toward familiar substrates or favorable published results.
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Separate historical and prospective data
Mark which outcomes came from literature or legacy screens and which were generated by the current campaign. This distinction helps reveal selection bias and lets you test whether the model improves on genuinely new experiments rather than merely fitting repeated records.
Fit a surrogate model and measure uncertainty
The surrogate is the fast model that scores the untested candidate pool. It does not need to be the most complex architecture available; it needs to produce useful predictions and uncertainty estimates quickly enough to guide the next batch.
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For a relatively small or tabular reaction dataset, an ensemble of tree models can be a practical baseline. In the prospective C–H borylation workflow, an XGBoost ensemble using ECFP4 fingerprints served as the active-learning oracle. After the dataset expanded, geometric graph neural networks were trained and evaluated as reaction-outcome predictors.
Compare models on the split that represents the intended deployment. A model that wins on a random split may fail when asked to predict a new chemical series. In the borylation study, geometric graph neural networks performed better than the strongest condition-aware XGBoost model on the final scaffold split: the reported mean Matthews correlation coefficient (MCC) range for the GNNs was 0.43–0.50, compared with 0.35 ± 0.12 for that XGBoost model. Those are study-specific results, not a guarantee for another transformation.
Check whether uncertainty is useful
Acquisition rules depend on uncertainty, so evaluate uncertainty separately from the mean prediction. A model can rank outcomes well while producing poorly calibrated confidence. Check whether predictions described as uncertain are actually more error-prone, and whether model disagreement identifies regions that differ chemically rather than merely reflecting noisy measurements.
Ensembles, Bayesian approximations, and dropout-based methods estimate uncertainty differently. Distinguish:
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- Epistemic uncertainty: uncertainty caused by limited knowledge or sparse coverage, which active learning can often reduce.
- Measurement or aleatoric uncertainty: variability inherent in the reaction or assay, which additional repeats may characterize but not eliminate.
Do not treat every high-uncertainty candidate as valuable. An uncertain reaction that is unsafe, unavailable, analytically ambiguous, or outside the campaign’s decision space should be filtered out before selection.
Choose an acquisition strategy that matches the campaign
An acquisition function converts model predictions and uncertainty into a ranking or selection score. The table below separates the commonly confused choices.
| Strategy | What it favors | Best fit | Main risk |
|---|---|---|---|
| Pure exploitation | Highest predicted outcome | Finding a strong candidate quickly when the model is already reliable | Can remain near known chemistry and miss better or broader regions |
| Uncertainty sampling | Most uncertain candidates | Learning where the model lacks information | May select irrelevant, infeasible, or noisy experiments |
| Expected improvement (EI) | Predicted improvement over the current best, weighted by the chance of achieving it | Single-objective optimization with a meaningful incumbent best | Depends on a credible predictive distribution and current-best definition |
| Upper confidence bound (UCB) | Predicted mean plus an uncertainty bonus | Explicitly tunable exploration–exploitation trade-offs | A poorly chosen uncertainty weight can over-explore or over-exploit |
| Bayesian Active Learning by Disagreement (BALD) | Cases expected to provide model-disagreement or information gain | Information-seeking campaigns where reducing epistemic uncertainty matters | Disagreement can reflect model misspecification rather than useful chemistry |
| EHVI-family methods | Expected improvement in a multi-objective Pareto frontier | Balancing yield, selectivity, cost, safety, or other competing outcomes | Requires clearly defined objectives, constraints, and preference handling |
Uncertainty sampling versus Bayesian optimization
Uncertainty sampling asks, in effect, “Which candidate would teach the model the most or reveal where it is weak?” Bayesian optimization asks, “Which candidate has the best expected value after accounting for uncertainty?” The two may select different experiments. Bayesian optimization includes exploitation explicitly; uncertainty sampling can select a low-predicted-value candidate because its label is informative.
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EI and UCB are Bayesian-optimization-style rules, but they encode the trade-off differently. EI emphasizes the probability and magnitude of beating the current best. UCB adds a controllable uncertainty term to the predicted mean. BALD is information-oriented: it uses disagreement among model hypotheses to favor candidates that could reduce epistemic uncertainty. These labels should not be treated as interchangeable.
Use constraints before ranking
Apply hard constraints first: available material, compatible equipment, safety limits, maximum solvent or catalyst use, analytical capacity, and any substrate that cannot be handled at the campaign scale. Then score the feasible pool. Otherwise, the acquisition function can spend its highest scores on experiments that will never be run.
Select a practical batch and run it carefully
Most laboratories run batches rather than one experiment at a time. Batch selection changes the optimization problem because the top-ranked candidates may be near-duplicates. Add diversity or clustering constraints when a batch should cover different scaffolds, condition regions, or mechanistic hypotheses.
- Generate the candidate pool. Enumerate untested substrate–condition combinations within the declared scope.
- Remove infeasible candidates. Apply material, safety, equipment, scale, and analytical constraints.
- Score the remaining candidates. Store predicted outcome, uncertainty, acquisition score, and the model version used.
- Deduplicate and diversify. Prevent a batch from being filled by near-identical structures unless replication is intentional.
- Review surprising selections. A chemist should check whether an apparently informative candidate is chemically interpretable and experimentally actionable.
- Run the batch and record actual conditions. Log deviations from the planned temperature, time, concentration, equivalents, atmosphere, work-up, and analysis.
The actual run record matters more than the intended recipe. A reaction carried out at a different temperature or concentration is a different labeled experiment and should not be silently assigned the planned condition vector.
Return results to the model and validate the right generalization
After analysis, append outcomes to the dataset, retrain or update the surrogate, recalculate uncertainty, and repeat the loop. Keep failed, ambiguous, and repeated experiments visible. If an outcome is missing because a sample was lost or an assay failed, mark it as missing rather than converting it to a chemical failure.
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Random splits often place close analogues in both training and test sets and can therefore look easier than prospective prediction. Use scaffold, chemical-series, temporal, or other blocked splits when the real decision involves new chemistry. The C–H borylation work compared random, Butina, and Bemis–Murcko scaffold splits; the more demanding scaffold evaluations exposed differences between the geometric GNNs and XGBoost.
Report the split, class balance, metric, and confidence interval or variation across repeats. For binary outcomes, MCC, balanced accuracy, precision–recall measures, and calibration can be more informative than raw accuracy. For continuous outcomes, report an error metric together with uncertainty calibration and the range of observed labels.
Do not confuse optimization with generalization
A rule that finds a high-yield reaction quickly may not produce a broadly useful prediction model. Conversely, an exploratory rule may spend experiments in uncertain regions that are not immediate optimization wins. State whether success means the best observed reaction, lower experimental burden, improved scaffold coverage, better out-of-domain prediction, or a combination.
What the prospective C–H borylation study demonstrates
A 2026 Nature Computational Science study provides a concrete prospective example, while remaining specific to C–H borylation. The initial dataset contained 518 substrates. The active-learning system scored a pool of 22,253 drug-like aromatic compounds, selected candidates over three prospective rounds, and tested 50 previously unreported substrates across approximately 96 reaction configurations.
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Across those active-learning rounds, 4,821 reactions were tested. The combined dataset reached 6,865 binary reaction outcomes covering 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 study.
The authors benchmarked ten acquisition functions and selected BALD for prospective screening. The result supports exploration as a practical way to expand chemical coverage in that task. It does not establish that BALD is the best rule for every reaction family, nor that the reported pool size, number of rounds, or scaffold increase should be expected elsewhere.
The workflow also illustrates why the experimental context belongs in the interpretation. It used high-throughput screening, LC–MS analysis, scale-up, and structural characterization. Those facilities enabled reliable feasibility and regioselectivity labels; another laboratory may need a different first-pass outcome or a smaller, slower loop.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How many experiments are needed?
There is no universal number of experiments that trains a useful reaction-prediction model. The required count depends on the size and diversity of the candidate space, the number of condition variables, outcome noise, class balance, and the generalization target.
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Plan the campaign around evidence rather than a fixed headline number:
- Start with data that spans the chemistry you expect to make decisions about, including informative negatives.
- Plot learning curves and prospective performance as each batch is added.
- Monitor whether uncertainty decreases in the regions that matter, not merely across the whole pool.
- Stop when additional experiments no longer change the decision, improve the target metric, or reduce consequential uncertainty.
- Reserve capacity for confirmation experiments and for candidates selected by a different strategy, so the campaign can distinguish genuine progress from acquisition-function bias.
The borylation study’s 6,865 outcomes are evidence of what one well-resourced, domain-specific campaign measured—not a minimum dataset size for reaction prediction in general.
Common failure modes and fixes
The model ranks familiar chemistry well but fails on new scaffolds
Cause: random validation allowed close analogues into both training and test sets.
Fix: use scaffold or chemical-series splits, track prospective performance, and include structurally diverse candidates in the acquisition batch.
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Cause: the acquisition function was allowed to rank candidates without feasibility, safety, material, or analytical filters.
Fix: apply hard constraints before scoring and have a chemist review the highest-information candidates.
Negative labels are misleading
Cause: an unreported or failed literature example was treated as proof of intrinsic inactivity, or condition differences were discarded.
Fix: tie every negative to the exact tested conditions and mark unknown outcomes as missing.
The acquisition function favors noisy measurements
Cause: assay variability or changing analytical procedures was mistaken for chemical uncertainty.
Fix: standardize measurement, record protocol changes, replicate strategically, and model measurement uncertainty separately from knowledge uncertainty.
Batch results are not reproducible
Cause: intended conditions were logged but deviations during execution were not.
Fix: capture actual temperature, time, concentration, equivalents, atmosphere, work-up, and analytical result for every run.
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Cause: yield, selectivity, cost, safety, or material use were collapsed into an undocumented weighted score.
Fix: state the weights and constraints, or use a multi-objective approach such as an EHVI-family acquisition rule and report the resulting trade-offs.
A practical implementation checklist
- Define the reaction decision and the first measurable outcome.
- Declare variable and fixed conditions, candidate-pool boundaries, and hard constraints.
- Standardize structures, units, condition fields, analytical methods, and missing-value codes.
- Keep negative outcomes and their tested scope.
- Choose a baseline surrogate and an uncertainty method that can be updated at laboratory speed.
- Evaluate both predictive performance and uncertainty calibration.
- Select exploitation, uncertainty, Bayesian-optimization, disagreement, or multi-objective acquisition according to the campaign objective.
- Control batch redundancy and preserve chemical diversity when coverage matters.
- Log the actual experiment and analysis, not just the planned recipe.
- Use validation splits that represent the chemistry the model must face next.
- Report whether the campaign optimized an observed reaction, reduced experimental burden, expanded scaffold coverage, or improved generalization.
When these choices are explicit, active learning becomes a defensible experimental program: each new reaction has a stated reason for being run, each result updates the next decision, and the final model is judged on the kind of chemistry it is expected to predict.
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