{"slug":"k-dense-ai-deepchem","source_name":"k-dense-ai/deepchem","name":"K Dense AI/Deepchem","description":"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.","version":1,"lift":{"pass_rate_delta_pts":40.91,"pass_rate_pct":100,"total_cases":22,"passed_cases":22,"tokens_delta_pct":123.3,"turns_delta_pct":0,"verdict":"pass","benchmark_model":"gemini-3.6-flash","grading_method":"judged","completed_at":"2026-08-07T16:10:47.023689+00:00"},"skill_score":1,"benchmark_models":[{"model":"gemini-3.6-flash","headline":true,"delta_pts":40.91,"with_pass_pct":100,"without_pass_pct":59.1,"tokens_delta_pct":123.3,"turns_delta_pct":0,"total_cases":22,"cases_aggregated":21,"verdict":"pass","never_hurt":true,"completed_at":"2026-08-07T16:10:47.023689+00:00","run_id":"5863f2aa-abdf-4a0a-a029-f71e0e6e62aa","version_number":1,"is_latest_version":true,"gate":null}],"trust":{"skill_safety":"passed","safety_status":"clean","intent_verdict":"safe","content_status":"clean","indexable":true},"license":"MIT license","install_count":0,"manifest_hash":"0f8636abedaacf8f841160d15b54cf1dd25330ff7a1bcdb763d808816d0b4c7b","raw_url":"https://app.decimal.ai/s/k-dense-ai-deepchem/SKILL.md","scorecard_url":"https://app.decimal.ai/skills/k-dense-ai-deepchem"}