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6 | 6 | # Chemical Representation Learning for Toxicity Prediction
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7 | 7 |
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8 | 8 |
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9 |
| -PyTorch implementation related to the paper *Chemical Representation Learning for Toxicity Prediction* (Born et al, 2023, Under review at *Digital Discovery*). |
| 9 | +PyTorch implementation related to the paper *Chemical Representation Learning for Toxicity Prediction* ([Born et al, 2023, *Digital Discovery*](https://pubs.rsc.org/en/content/articlehtml/2023/dd/d2dd00099g)). |
10 | 10 | ## Training your own model
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11 | 11 |
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12 | 12 | The library itself has few dependencies (see [setup.py](setup.py)) with loose requirements.
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@@ -58,13 +58,17 @@ In [notebooks/toxicity_attention.ipynb](notebooks/toxicity_attention.ipynb) we s
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58 | 58 |
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59 | 59 |
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60 | 60 | ## Citation
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61 |
| -If you use this code in your projects, please (temporarily) cite the following (full paper in review): |
| 61 | +If you use this code in your projects, please cite the following: |
62 | 62 |
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63 | 63 | ```bib
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64 | 64 | @article{born2023chemical,
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65 |
| - title={Chemical representation learning for toxicity prediction}, |
66 |
| - author={Born, Jannis and Markert, Greta and Janakarajan, Nikita and Kimber, Talia B. and Volkamer, Andrea and Rodriguez Martinez, Maria and Manica, Matteo}, |
67 |
| - journal={Under review at Digital Discovery}, |
68 |
| - year={2023} |
| 65 | + author = {Born, Jannis and Markert, Greta and Janakarajan, Nikita and Kimber, Talia B. and Volkamer, Andrea and Martínez, María Rodríguez and Manica, Matteo}, |
| 66 | + title = {Chemical representation learning for toxicity prediction}, |
| 67 | + journal = {Digital Discovery}, |
| 68 | + year = {2023}, |
| 69 | + pages = {-}, |
| 70 | + publisher = {RSC}, |
| 71 | + doi = {10.1039/D2DD00099G}, |
| 72 | + url = {http://dx.doi.org/10.1039/D2DD00099G} |
69 | 73 | }
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70 | 74 | ```
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