MolPILE -- large-scale, diverse dataset for molecular representation learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909805720895488 |
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| author | Adamczyk, Jakub Poziemski, Jakub Job, Franciszek Król, Mateusz Makowski, Maciej |
| author_facet | Adamczyk, Jakub Poziemski, Jakub Job, Franciszek Król, Mateusz Makowski, Maciej |
| contents | The size, diversity, and quality of pretraining datasets critically determine the generalization ability of foundation models. Despite their growing importance in chemoinformatics, the effectiveness of molecular representation learning has been hindered by limitations in existing small molecule datasets. To address this gap, we present MolPILE, large-scale, diverse, and rigorously curated collection of 222 million compounds, constructed from 6 large-scale databases using an automated curation pipeline. We present a comprehensive analysis of current pretraining datasets, highlighting considerable shortcomings for training ML models, and demonstrate how retraining existing models on MolPILE yields improvements in generalization performance. This work provides a standardized resource for model training, addressing the pressing need for an ImageNet-like dataset in molecular chemistry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18353 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MolPILE -- large-scale, diverse dataset for molecular representation learning Adamczyk, Jakub Poziemski, Jakub Job, Franciszek Król, Mateusz Makowski, Maciej Machine Learning The size, diversity, and quality of pretraining datasets critically determine the generalization ability of foundation models. Despite their growing importance in chemoinformatics, the effectiveness of molecular representation learning has been hindered by limitations in existing small molecule datasets. To address this gap, we present MolPILE, large-scale, diverse, and rigorously curated collection of 222 million compounds, constructed from 6 large-scale databases using an automated curation pipeline. We present a comprehensive analysis of current pretraining datasets, highlighting considerable shortcomings for training ML models, and demonstrate how retraining existing models on MolPILE yields improvements in generalization performance. This work provides a standardized resource for model training, addressing the pressing need for an ImageNet-like dataset in molecular chemistry. |
| title | MolPILE -- large-scale, diverse dataset for molecular representation learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.18353 |