File size: 3,227 Bytes
7eafb33 04f5fd8 895a807 ccbdbb4 895a807 ccbdbb4 895a807 ccbdbb4 82457c0 ccbdbb4 895a807 ccbdbb4 82457c0 ccbdbb4 fbb3cb6 ccbdbb4 5d27fa6 895a807 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
# 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.
# 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 [their GitHub repo](https://github.com/microsoft/molecule-generation).
**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)](https://openreview.net/forum?id=ZTsoE8G3GG).
**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)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
## Citation
```bib
@inproceedings{maziarz2021learning,
author={Krzysztof Maziarz and Henry Richard Jackson{-}Flux and Pashmina Cameron and
Finton Sirockin and Nadine Schneider and Nikolaus Stiefl and Marwin H. S. Segler and Marc Brockschmidt},
title = {Learning to Extend Molecular Scaffolds with Structural Motifs},
booktitle = {The Tenth International Conference on Learning Representations, {ICLR}},
year = {2022}
}
```
|