Train molecular property prediction models with DeepChem loaders, featurizers, and splitters.
npx skills add https://github.com/dralkh/seerai --skill deepchem-dralkh
Please help me install this Agent Skill. Skill: deepchem Source: https://github.com/dralkh/seerai/tree/main/skills/deepchem Command: npx skills add https://github.com/dralkh/seerai --skill deepchem-dralkh
DeepChem removes the complexity of building molecular machine learning pipelines by giving you ready-made tools for featurization, dataset loading, scaffold-aware splitting, model training, evaluation, and transfer learning.
Use the deepchem skill to train a scaffold-split molecular property model on your dataset and return evaluation metrics and predictions.
High-intent search queries and answers about installing and using this skill.
Molecular property prediction uses ready-made DeepChem loaders, featurizers, and splitters to train models for solubility, toxicity, and ADMET tasks. You can build predictors by applying scaffold-aware splitting to your molecular data.
Scaffold splitting is a dataset partitioning method that separates molecules by their underlying chemical scaffolds. It is needed for molecular machine learning to ensure structurally distinct compounds are held out, providing rigorous model evaluation.
Yes, you can fine-tune pretrained models like ChemBERTa, GROVER, and MolFormer for small chemistry datasets. Transfer learning adapts these existing molecular representations to your specific property prediction tasks with less training data.
Yes, the workflow supports graph neural networks including GCN, GAT, AttentiveFP, and DMPNN. These GNN models process molecular graph structures directly using PyTorch, TensorFlow, or JAX backends for property prediction.
You need PyTorch, TensorFlow, or JAX backends to run MoleculeNet benchmarking experiments. These frameworks support the selected model family required to execute standard evaluation patterns on MoleculeNet datasets.
The best way to evaluate ADMET prediction models is using MoleculeNet datasets and standard evaluation patterns. This approach applies DeepChem metrics and transformers to generate reproducible benchmarking results for your chemistry tasks.
jaechang-hitsTrain and predict molecular properties using DeepChem with PyTorch or TensorFlow.
NVIDIA-BioNeMoExecute KERMT model inference workflows on SMILES chemical datasets.
hxk622Train machine learning models for molecular property prediction and drug discovery.
Yezez9Perform molecular property prediction and featurization using DeepChem.
