Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
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| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.5 latest | 2026-09-02 | 1e5eeff | 10,754 B | A | view · diff |
| v1.4 | 2026-07-28 | 4fb7e0b | 9,796 B | A | view · diff |
| v1.2 | 2026-07-26 | 2f2022d | 9,796 B | A | view · diff |
| v1.1 | 2026-07-26 | b085e11 | 19,498 B | A | view · diff |
| git:20260611.1b8fae3 | 2026-06-11 | 1b8fae3 | 19,503 B | A | view |
k-dense-ai/scientific-agent-skills · 42,925 stars · license MIT · pushed 2026-09-02 · branch main
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