update readme

Author: sfluegel05

README.md

@@ -2,7 +2,7 @@
 An AI ensemble model for predicting chemical classes in the ChEBI ontology. It integrates deep learning models,
 rule-based models and generative AI-based models.
 
-A web application for the ensemble is available at https://chebifier.hastingslab.org/.
+A web application for Chebifier is available at https://chebifier.hastingslab.org/.
 
 ## Installation
 
@@ -38,23 +38,27 @@ The package provides a command-line interface (CLI) for making predictions using
 The ensemble configuration is given by a configuration file (by default, this is `chebifier/ensemble.yml`). If you
 want to change which models are included in the ensemble or how they are weighted, you can create your own configuration file.
 
-Model weights for deep learning models are automatically downloaded from [Hugging Face](https://huggingface.co/chebai).
-To use specific model weights from Hugging face, add the `load_model` key in your configuration file. For example:
+Trained deep learning models are automatically downloaded from [Hugging Face](https://huggingface.co/chebai).
+To access a model from Hugging face, add the `load_model` key in your configuration file. For example:
 
 ```yaml
 my_electra:
   type: electra
-  load_model: "electra_chebi50_v241"
+  load_model: "electra_chebi50-3star_v244"
 ```
 
 ### Available model weights:
 
+* `resgated-aug_chebi50-3star_v244`
+* `gat-aug_chebi50_v244`
+* `electra_chebi50-3star_v244`
+* `gat_chebi50_v244`
 * `electra_chebi50_v241`
 * `resgated_chebi50_v241`
 * `c3p_with_weights`
 
 
-However, you can also supply your own model checkpoints (see `configs/example_config.yml` for an example).
+You can also supply your own model checkpoints (see `configs/example_config.yml` for an example).
 
 ```bash
 # Make predictions
@@ -72,12 +76,12 @@ python -m chebifier predict --help
 
 ### Python API
 
-You can also use the package programmatically:
+You can use the package programmatically as well:
 
 ```python
 from chebifier import BaseEnsemble
 
-# Instantiate ensemble model. If desired, can pass
+# Instantiate ensemble model. Optionally, you can pass
 # a path to a configuration, like 'configs/example_config.yml'
 ensemble = BaseEnsemble()
 
@@ -100,11 +104,12 @@ Currently, the following models are supported:
 
 | Model | Description | #Classes | Publication                                                           | Repository                                                                            |
 |-------|-------------|----------|-----------------------------------------------------------------------|----------------------------------------------------------------------------------------|
-| `electra` | A transformer-based deep learning model trained on ChEBI SMILES strings. | 1522 | [Glauer, Martin, et al., 2024: Chebifier: Automating semantic classification in ChEBI to accelerate data-driven discovery, Digital Discovery 3 (2024) 896-907](https://pubs.rsc.org/en/content/articlehtml/2024/dd/d3dd00238a) | [python-chebai](https://github.com/ChEB-AI/python-chebai) |
-| `resgated` | A Residual Gated Graph Convolutional Network trained on ChEBI molecules. | 1522 | | [python-chebai-graph](https://github.com/ChEB-AI/python-chebai-graph) |
+| `electra` | A transformer-based deep learning model trained on ChEBI SMILES strings. | 1531*  | [Glauer, Martin, et al., 2024: Chebifier: Automating semantic classification in ChEBI to accelerate data-driven discovery, Digital Discovery 3 (2024) 896-907](https://pubs.rsc.org/en/content/articlehtml/2024/dd/d3dd00238a) | [python-chebai](https://github.com/ChEB-AI/python-chebai) |
+| `resgated` | A Residual Gated Graph Convolutional Network trained on ChEBI molecules. | 1531* | | [python-chebai-graph](https://github.com/ChEB-AI/python-chebai-graph) |
+| `gat` | A Graph Attention Network trained on ChEBI molecules. | 1531* | | [python-chebai-graph](https://github.com/ChEB-AI/python-chebai-graph) |
 | `chemlog_peptides` | A rule-based model specialised on peptide classes. | 18 | [Flügel, Simon, et al., 2025: ChemLog: Making MSOL Viable for Ontological Classification and Learning, arXiv](https://arxiv.org/abs/2507.13987) | [chemlog-peptides](https://github.com/sfluegel05/chemlog-peptides) |
 | `chemlog_element`, `chemlog_organox` | Extensions of ChemLog for classes that are defined either by the presence of a specific element or by the presence of an organic bond. | 118 + 37 | | [chemlog-extra](https://github.com/ChEB-AI/chemlog-extra) |
-| `c3p` | A collection _Chemical Classifier Programs_, generated by LLMs based on the natural language definitions of ChEBI classes. | 338 | [Mungall, Christopher J., et al., 2025: Chemical classification program synthesis using generative artificial intelligence, arXiv](https://arxiv.org/abs/2505.18470) | [c3p](https://github.com/chemkg/c3p) |
+| `c3p` | A collection _Chemical Classifier Programs_, generated by LLMs based on the natural language definitions of ChEBI classes. | 338 | [Mungall, Christopher J., et al., 2025: Chemical classification program synthesis using generative artificial intelligence, Journal of Cheminsformatics](https://link.springer.com/article/10.1186/s13321-025-01092-3) | [c3p](https://github.com/chemkg/c3p) |
 
 In addition, Chebifier also includes a ChEBI lookup that automatically retrieves the ChEBI superclasses for a class
 matched by a SMILES string. This is not activated by default, but can be included by adding
@@ -116,6 +121,8 @@ chebi_lookup:
 to your configuration file.
 
 ### The ensemble
+For an extended description of the ensemble, see [Flügel, Simon, et al., 2025: Chebifier 2: An Ensemble for Chemistry](https://ceur-ws.org/Vol-4064/SymGenAI4Sci-paper4.pdf).
+
 <img width="700" alt="ensemble_architecture" src="https://github.com/user-attachments/assets/9275d3cd-ac88-466f-a1e9-27d20d67543b" />
 
 Given a sample (i.e., a SMILES string) and models $m_1, m_2, \ldots, m_n$, the ensemble works as follows:
@@ -146,20 +153,18 @@ Therefore, if in doubt, we are more confident in the negative prediction.
 
 Confidence can be disabled by the `use_confidence` parameter of the predict method (default: True).
 
-The model_weight can be set for each model in the configuration file (default: 1). This is used to favor a certain
+The`model_weight` can be set for each model in the configuration file (default: 1). This is used to favor a certain
 model independently of a given class.
-Trust is based on the model's performance on a validation set. After training, we evaluate the Machine Learning models
-on a validation set for each class. If the `ensemble_type` is set to `wmv-f1`, the trust is calculated as 1 + the F1 score.
+`Trust` is based on the model's performance on a validation set. After training, we evaluate the Machine Learning models
+on a validation set for each class. If the `ensemble_type` is set to `wmv-f1`, the trust is calculated as $F1^6.25$.
 If the `ensemble_type` is set to `mv` (the default), the trust is set to 1 for all models.
 
 ### Inconsistency resolution
 After a decision has been made for each class independently, the consistency of the predictions with regard to the ChEBI hierarchy
 and disjointness axioms is checked. This is
 done in 3 steps:
 - (1) First, the hierarchy is corrected. For each pair of classes $A$ and $B$ where $A$ is a subclass of $B$ (following
-the is-a relation in ChEBI), we set the ensemble prediction of $B$ to 1 if the prediction of $A$ is 1. Intuitively
-speaking, if we have determined that a molecule belongs to a specific class (e.g., aromatic primary alcohol), it also
-belongs to the direct and indirect superclasses (e.g., primary alcohol, aromatic alcohol, alcohol).
+the is-a relation in ChEBI), we set the ensemble prediction of $A$ to $0$ if the _absolute value_ of $B$'s score is large than that of $A$. For example, if $A$ has a net score of $3$ and $B$ has a net score of $-4$, the ensemble will set $A$ to $0$ (i.e., predict neither $A$ nor $B$).
 - (2) Next, we check for disjointness. This is not specified directly in ChEBI, but in an additional ChEBI module ([chebi-disjoints.owl](https://ftp.ebi.ac.uk/pub/databases/chebi/ontology/)).
 We have extracted these disjointness axioms into a CSV file and added some more disjointness axioms ourselves (see
 `data>disjoint_chebi.csv` and `data>disjoint_additional.csv`). If two classes $A$ and $B$ are disjoint and we predict