{"slug":"lingxling-deepchem","source_name":"lingxling/deepchem","name":"Lingxling/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":13.04,"pass_rate_pct":95.7,"total_cases":23,"passed_cases":22,"tokens_delta_pct":205.3,"turns_delta_pct":0,"verdict":"mixed","benchmark_model":"gemini-3.6-flash","grading_method":"judged","completed_at":"2026-08-27T16:30:14.972842+00:00"},"skill_score":null,"benchmark_models":[{"model":"gemini-3.6-flash","headline":true,"delta_pts":13.04,"with_pass_pct":95.7,"without_pass_pct":82.6,"tokens_delta_pct":205.3,"turns_delta_pct":0,"total_cases":23,"cases_aggregated":23,"verdict":"mixed","never_hurt":false,"completed_at":"2026-08-27T16:30:14.972842+00:00","run_id":"e53c95f0-9161-4bad-8d74-07a02bd43bd4","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":"08bdace752dc7cca5b8620a34bd4ab44db275ca60b8492feee6d212cbba32493","raw_url":"https://app.decimal.ai/s/lingxling-deepchem/SKILL.md","scorecard_url":"https://app.decimal.ai/skills/lingxling-deepchem"}