update

model_cards/article.md

@@ -1,21 +1,42 @@
-# MoLeR -- Documentation
+### Model card - MoLeR
 
-## Parameters
+**Model Details**: MoLeR is a graph-based molecular generative model that can be conditioned (primed) on scaffolds. The model decorates scaffolds with realistic structural motifs.
 
-### Algorithm Version:
-Which model checkpoint to use (trained on different datasets).
+**Developers**: Krzysztof Maziarz and co-authors from Microsoft Research and Novartis (full reference at bottom).
 
-### Scaffolds
-One or multiple scaffolds (or seed molecules), provided as '.'-separated SMILES. If empty, no scaffolds are used.
+**Distributors**: Developer's code wrapped and distributed by GT4SD Team (2023) from IBM Research.
 
-### Number of samples:
-How many samples should be generated (between 1 and 50). 
+**Model date**: Released around March 2022.
 
-### Beam size
-Beam size used in beam search decoding (the higher the slower but better).
+**Model version**: Model provided by original authors, see [their GitHub repo](https://github.com/microsoft/molecule-generation).
 
-### Seed
-The random seed used for initialization.
+**Model type**: An encoder-decoder-based GNN for molecular generation.
+
+**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: Trained by the original authors with the default parameters provided [on GitHub](https://github.com/microsoft/molecule-generation).
+
+**Paper or other resource for more information**: Learning to Extend Molecular Scaffolds with Structural Motifs (ICLR 2022).
+
+**License**: MIT
+
+**Where to send questions or comments about the model**: Open an issue on original author's [GitHub repository](https://github.com/microsoft/molecule-generation).
+
+**Intended Use. Use cases that were envisioned during development**: Chemical research, in particular drug discovery.
+
+**Primary intended uses/users**: Researchers and computational chemists using the model for model comparison or research exploration purposes.
+
+**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
+
+**Factors**: Not applicable.
+
+**Metrics**: Validation loss on decoding correct molecules. Evaluated on several downstream tasks.
+
+**Datasets**: 1.5M drug-like molecules from GuacaMol benchmark. Finetuning on 20 molecular optimization tasks from GuacaMol.
+
+**Ethical Considerations**: Unclear, please consult with original authors in case of questions.
+
+**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.
+
+Model card prototype inspired by [*Mitchell et al. (2019),  Proceedings of the Conference on Fairness, Accountability, and Transparency*](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
 
 ## Citation
 

model_cards/description.md

@@ -1,27 +1,23 @@
 
 # MoLeR (MOlecule-LEvel Representation)
 
-<img src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="800">
-
-### Model card
-
-*Model Details*: MoLeR is a graph-based molecular generative model that can be conditioned (primed) on scaffolds. The model decorates scaffolds with realistic structural motifs.
-*Developers*: Krzysztof Maziarz and co-authors from Microsoft Research and Novartis (full reference at bottom).
-*Distributors*: Developer's code wrapped and distributed by GT4SD Team (2023) from IBM Research.
-*Model date*: Released around March 2022.
-*Model version*: Model provided by original authors, see:
-*Model type*: An encoder-decoder-based GNN for molecular generation.
-*Information about training algorithms, parameters, fairness constraints or other applied approaches, and features*: Trained by the original authors with the default parameters provided [on GitHub](https://github.com/microsoft/molecule-generation).
-*Paper or other resource for more information*: Learning to Extend Molecular Scaffolds with Structural Motifs (ICLR 2022).
-*License*: MIT
-*Where to send questions or comments about the model*: Open an issue on original author's [GitHub repository](https://github.com/microsoft/molecule-generation).
-*Intended Use. Use cases that were envisioned during development*: Chemical research, in particular drug discovery.
-*Primary intended uses/users*: Researchers and computational chemists using the model for model comparison or research exploration purposes.
-*Out-of-scope use cases*: Production-level inference, producing molecules with harmful properties.
-*Factors*: Not applicable.
-*Metrics*: Validation loss on decoding correct molecules. Evaluated on several downstream tasks.
-*Datasets*: 1.5M drug-like molecules from GuacaMol benchmark. Finetuning on 20 molecular optimization tasks from GuacaMol.
-*Ethical Considerations*: Unclear, please consult with original authors in case of questions.
-*Caveats and Recommendations*: Unclear, please consult with original authors in case of questions.
-
-Model card prototype inspired by [*Mitchell et al. (2019),  Proceedings of the Conference on Fairness, Accountability, and Transparency*](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
+<img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="80" >
+This model is provided and distributed by the **GT4SD** (Generative Toolkit for Scientific Discovery).
+
+
+## Model documentation & parameters
+
+### Algorithm Version:
+Which model checkpoint to use (trained on different datasets).
+
+### Scaffolds
+One or multiple scaffolds (or seed molecules), provided as '.'-separated SMILES. If empty, no scaffolds are used.
+
+### Number of samples:
+How many samples should be generated (between 1 and 50). 
+
+### Beam size
+Beam size used in beam search decoding (the higher the slower but better).
+
+### Seed
+The random seed used for initialization.