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Fix README links #827

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f868c78
Fix broken README navigation links
AdrianSosic Jun 12, 2026
46c812e
Add API reference links to code components in README
AdrianSosic Jun 12, 2026
289069f
Fix invalid self-closing anchor tags in README
AdrianSosic Jun 18, 2026
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66 changes: 41 additions & 25 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -15,13 +15,11 @@
[![Logo](https://raw.githubusercontent.com/emdgroup/baybe/main/docs/_static/banner2.svg)](https://github.com/emdgroup/baybe/)

 
<a href="https://emdgroup.github.io/baybe/">Homepage<a/>
<a href="https://emdgroup.github.io/baybe/">Documentation</a>
&nbsp;•&nbsp;
<a href="https://emdgroup.github.io/baybe/stable/userguide/userguide.html">User Guide<a/>
<a href="https://emdgroup.github.io/baybe/stable/_autosummary/baybe.html">API Reference</a>
&nbsp;•&nbsp;
<a href="https://emdgroup.github.io/baybe/stable/_autosummary/baybe.html">Documentation<a/>
&nbsp;•&nbsp;
<a href="https://emdgroup.github.io/baybe/stable/misc/contributing_link.html">Contribute<a/>
<a href="https://emdgroup.github.io/baybe/stable/misc/contributing_link.html">Contribute</a>
&nbsp;
</div>

Expand Down Expand Up @@ -143,8 +141,10 @@ For more information on this step, see our

### Defining the Optimization Objective

In BayBE's language, the reaction yield can be represented as a `NumericalTarget`,
which we wrap into a `SingleTargetObjective`:
In BayBE's language, the reaction yield can be represented as a
[`NumericalTarget`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.targets.numerical.NumericalTarget.html),
which we wrap into a
[`SingleTargetObjective`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.objectives.single.SingleTargetObjective.html):

```python
from baybe.targets import NumericalTarget
Expand All @@ -153,18 +153,21 @@ from baybe.objectives import SingleTargetObjective
target = NumericalTarget(name="Yield")
objective = SingleTargetObjective(target=target)
```
In cases where we are confronted with multiple (potentially conflicting) targets
(e.g., yield vs selectivity),
the `ParetoObjective` or `DesirabilityObjective` can be used to define how the targets should be balanced.
For more details, see the
In cases where we are confronted with multiple (potentially conflicting) targets (e.g.,
yield vs selectivity), the
[`ParetoObjective`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.objectives.pareto.ParetoObjective.html)
or
[`DesirabilityObjective`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.objectives.desirability.DesirabilityObjective.html)
can be used to define how the targets should be balanced. For more details, see the
[objectives section](https://emdgroup.github.io/baybe/stable/components/objectives.html)
of the user guide.

### Defining the Search Space

Next, we inform BayBE about the available "control knobs", that is, the underlying
reaction parameters we can tune to optimize the yield.
In this case we tune granularity, pressure and solvent, each being encoded as a `Parameter`.
reaction parameters we can tune to optimize the yield. In this case we tune granularity,
pressure and solvent, each being encoded as a
[`Parameter`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.parameters.base.Parameter.html).
We also need to specify which values individual parameters can take.

```python
Expand Down Expand Up @@ -208,7 +211,8 @@ relationships between our parameters. Details can be found in the
In this example, we assume no further constraints.

With the parameter definitions at hand, we can now create our
`SearchSpace` based on the Cartesian product of all possible parameter values:
[`SearchSpace`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.searchspace.core.SearchSpace.html)
based on the Cartesian product of all possible parameter values:

```python
from baybe.searchspace import SearchSpace
Expand All @@ -226,7 +230,7 @@ As an optional step, we can specify details on how the optimization of the exper
performed. If omitted, BayBE will choose a default Bayesian optimization setting.

For our example, we combine two recommenders via a so-called meta recommender named
`TwoPhaseMetaRecommender`:
[`TwoPhaseMetaRecommender`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender.html):

1. In cases where no measurements have been made prior to the interaction with BayBE,
the parameters will be recommended with the `initial_recommender`.
Expand All @@ -252,7 +256,9 @@ of the user guide.

### The Optimization Loop

We can now construct a `Campaign` that performs the Bayesian optimization of the experimental configurations:
We can now construct a
[`Campaign`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.campaign.Campaign.html)
that performs the Bayesian optimization of the experimental configurations:

```python
from baybe import Campaign
Expand Down Expand Up @@ -288,7 +294,9 @@ measure the reaction yield.
29 fine 5.0 Solvent B
```

Next, we need to conduct the recommended experiments and record the corresponding `Target` values.
Next, we need to conduct the recommended experiments and record the corresponding
[`Target`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.targets.base.Target.html)
values.

```python
df["Yield"] = [
Expand All @@ -305,14 +313,17 @@ print(df)
29 fine 5.0 Solvent B 59.4
```

Now, we can add the newly measured `Target` values to the `Campaign`:
Now, we can add the newly measured
[`Target`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.targets.base.Target.html)
values to the
[`Campaign`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.campaign.Campaign.html):

```python
campaign.add_measurements(df)
```

With the newly provided data, BayBE can produce a refined recommendation for the next iteration.
This loop typically continues until a desired `Target` value is achieved in the experiment.
This loop typically continues until a desired [`Target`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.targets.base.Target.html) value is achieved in the experiment.

### Inspect the Progress of the Experimental Configuration Optimization

Expand All @@ -322,14 +333,19 @@ by tuning the solvent, base and ligand
Each line shows the best target value that was cumulatively achieved after a given number of experimental iterations.


Different lines show outcomes of `Campaigns` with different settings.
Different lines show outcomes of
[`Campaign`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.campaign.Campaign.html)s
with different settings.

![Substance Encoding Example](./examples/Backtesting/full_lookup_light.svg)

In particular, the five `Campaigns` differ in how molecules are encoded within
each chemical `Parameter`. Instead of simply one-hot encoding each SMILES string,
`SubstanceParameter` can be used to directly compute chemical fingerprints from
the input SMILES.
In particular, the five
[`Campaign`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.campaign.Campaign.html)s
differ in how molecules are encoded within each chemical
[`Parameter`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.parameters.base.Parameter.html).
Instead of simply one-hot encoding each SMILES string,
[`SubstanceParameter`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.parameters.substance.SubstanceParameter.html)
can be used to directly compute chemical fingerprints from the input SMILES.
We can see that optimization is more efficient when
using chemical encodings (e.g., *MORDRED*) rather than encoding categories with *one-hot* encoding. The latter is, in fact, no better than *randomly* suggesting parameter configurations at each experimental iteration.

Expand Down Expand Up @@ -396,7 +412,7 @@ pip install 'baybe[chem,simulation]'
The available groups are:
- `extras`: Installs all dependencies required for optional features.
- `benchmarking`: Required for running the benchmarking module.
- `chem`: Cheminformatics utilities (e.g. for the `SubstanceParameter`).
- `chem`: Cheminformatics utilities (e.g. for the [`SubstanceParameter`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.parameters.substance.SubstanceParameter.html)).
- `docs`: Required for creating the documentation.
- `examples`: Required for running the examples/streamlit.
- `lint`: Required for linting and formatting.
Expand Down
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