Property prediction
Search representations and model families for molecular, protein, cellular, or assay-level properties.
Autoresearch for biological data and scientific objectives.
Define the model and transformation space Reverie may explore, connect the relevant datasets, and optimize against the metrics that matter to your scientific program.
Where Reverie fits
Model research for data-rich biological programs.
Reverie is useful when the scientific objective can be measured, multiple modeling approaches are plausible, and the cost of manually coordinating the search limits how much your team can explore.
Response modeling
Train models that predict experimental response across samples, conditions, compounds, or interventions.
Candidate ranking
Optimize models that prioritize candidates for further computational or experimental review.
Search more model directions without turning every experiment into a manual project.
Scientific objective
Specify the predictive, classificatory, ranking, or representation objective and its constraints.
Biological data
Work with structured measurements, sequences, images, assays, or multimodal datasets.
Research space
Define candidate architectures, features, preprocessing, losses, and training procedures.
GPU experiments
Train and evaluate model directions while Reverie uses prior outcomes to choose the next experiments.
Scientific outputs
Receive trained models, comparative results, artifacts, and a report of the full research path.
Example project
Improve molecular property prediction with a controlled model search.
This illustrates the level of specification Reverie can work from; each project is configured around the team’s actual scientific question.
- Objective
- Improve held-out predictive performance while preserving calibration and inference constraints.
- Dataset
- Molecular structures, assay readouts, experimental metadata, and defined train-validation-test groups.
- Action space
- Featurizers, graph and sequence architectures, loss functions, sampling strategies, augmentation, and training parameters.
- Evaluation
- Grouped or scaffold-aware validation, calibration, error analysis, robustness checks, and compute efficiency.
- Outputs
- Trained candidate models, reproducible configurations, evaluation artifacts, and a comparative research report.
Built around
scientific constraints.
The best metric is rarely the only requirement. Reverie can evaluate candidate models against the validation structure, resource limits, and domain constraints your team defines.
Flexible data. Structure the project around the modalities and splits appropriate to the scientific question.
Controlled search. Bound the methods and transformations Reverie is allowed to explore.
Traceable conclusions. Connect every reported result to its model, configuration, data path, and evaluation.
Delivered to your team
Models and evidence, not just an experiment log.
Trained artifacts
Model weights, preprocessing state, configurations, checkpoints, and the environment required to reproduce them.
Scientific comparison
Side-by-side metrics, subgroup analysis, failure modes, and the tradeoffs among leading candidates.
Research report
A readable account of what Reverie explored, what it learned, and why the selected models were retained.