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license: cc-by-4.0 |
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--- |
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# Synthetic mass spectra dataset No. 2 |
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This is the second iteration of the GC-EI-MS synthetic dataset generated by [NEIMS] and [RASSP] models from 4.8 million structures downloaded from [ZINC 20]. |
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The overlap with version 1 is approximately 8k structures (negligible), so it's usable as an extension to synth1. |
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## Spectra generators |
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For generating this dataset, we used custom-trained NEIMS and RASSP models. We did this step because RASSP model is not publicly available and we wanted to |
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have more control over potential dataleaks in the context of further models stemming from this dataset. For better reproducibility we make the NIST splits |
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used for training NEIMS and RASSP models available in our [GitHub repository] (#TODO) |
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## Dataset creation |
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To uniformly cover the chemical space, we first scraped 1.8 billion SMILES strings from the ZINC20 library using the 2D-standard-annotated-druglike query. |
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From this dataset, we extracted a random sample of 30 million noncorrupted SMILES strings shorter than 100 characters. Further, we canonicalized, deduplicated, |
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and stripped the SMILES of stereochemical information, as stereochemistry is not reliably translated into spectra and cannot be accurately detected. |
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Additionally, we removed all NIST 20 compounds to prevent data leakage during the testing of our trained model in the future. |
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To evaluate the impact of RASSP and NEIMS respective spectrum prediction capabilities on SpecTUS performance, we created two identical sets of spectra generated |
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by each model. RASSP's strict input filters reduced the dataset to approximately 4.8 million compounds. In total, the pretraining datasets comprises 4.8 million |
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unique compounds and 9.6 million unique spectra. |
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Lastly, we split each synthetic dataset into training, validation, and test sets using a 0.9:0.05:0.05 ratio. The splitting process was random, but corresponding splits |
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(training, validation, and test sets) for the NEIMS and RASSP-generated spectra contained the same compounds. |
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## Size |
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```text |
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rassp_custom_gen/train.jsonl 4364744 |
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rassp_custom_gen/valid.jsonl 242486 |
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rassp_custom_gen/test.jsonl 242486 |
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TOTAL 4849716 |
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neims_custom_gen/train.jsonl 4364748 |
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neims_custom_gen/valid.jsonl 242483 |
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neims_custom_gen/test.jsonl 242485 |
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TOTAL 4849716 |
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``` |
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<!-- |
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## Dataset choice |
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If you're choosing between synth1 and synth2 (and don't need both), choose synth2. The origin of synth2 is better documented in the 3_datapreprocessing.ipynb notebook in our [GitHub repository], |
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even though the creation process is in essence the same for both datasets. |
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There are the same compound in the splits of both RASSP and NEIMS generated datasets but some |
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duplicities emerged, so the sets are not identical (!the scale of the leak is less than 4 compounds in almost 5M set, and the test and validation sets are not used for |
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the final testing and validation in our paper, so we call it negligible!) |
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--> |
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## Data format |
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Each line of every file is a `json` comprising three items: |
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```text |
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{"intensity":[0.01,0.08,0.06,0.05 ... 0.02,0.79,0.34,0.12], |
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"mz":[18,27,28,29,38,39,40,41,42,43, ... 202,203,270,271,272,299,300,301], |
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"smiles":"CCC(C)C1CCCCN1C(=O)CNc1cccc(C#N)c1"} |
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``` |
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From the nature of the NEIMS spectra predictor, all NEIMS-generated peak intensities are in 2 decimals precision. RASSP generated intensities are not restricted in this way, |
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so to save storage we rounded them up to 6 decimal places which is beyond recognition for our model in the downstream task. |
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Our [preprint] (TODO) provides more information about the task background, the final finetuned model, and the experiments. |
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[NEIMS]: https://github.com/brain-research/deep-molecular-massspec |
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[RASSP]: https://github.com/thejonaslab/rassp-public |
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[ZINC 20]: https://zinc20.docking.org/ |
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[GitHub repository]: TODO |
