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deepchem

Convert SMILES, SDF, and sequence inputs into featurized datasets for molecular property prediction.

Updated May 24, 2026
Search Tags:#featurization#deepchem#gnn#scaffold splitting#transfer learning#molecular property prediction#molecular ml
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill deepchem-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deepchem
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/deepchem
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill deepchem-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires deepchem, numpy, sys, argparse, and includes scripts (resource) and references (resource) components.

What problem does it solve?

DeepChem helps you transform chemical and biological inputs into ML-ready representations and then train/evaluate models to predict molecular properties, such as ADMET, toxicity, and solubility, with minimal boilerplate.

Core Features & Use Cases

  • Molecular data loading: Ingest SMILES/SDF/FASTA inputs and build datasets for downstream learning.
  • Diverse featurization options: Generate fingerprints, descriptors, and graph features for both traditional ML and GNNs.
  • Robust dataset splitting: Use scaffold-based splitting to reduce leakage in drug-discovery-style evaluation.
  • Model training & evaluation: Train classical models, multitask regressors/classifiers, and pretrained transformer/GNN approaches on MoleculeNet benchmarks.
  • Transfer learning: Fine-tune ChemBERTa/GROVER/MolFormer-style pipelines for small datasets and new scaffolds.

Use case example: You have a CSV of molecules with SMILES and a target like solubility, and you want a leakage-resistant training setup plus quick baseline models and metrics.

Quick Start

Use the deepchem skill to train a solubility predictor on Delaney (ESOL) and evaluate it end-to-end.

Frequently Asked Questions about deepchem

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I predict molecular properties from SMILES strings?▼

To predict molecular properties from SMILES strings, you can convert the inputs into featurized datasets and train appropriate ML or GNN models. This pipeline supports ADMET, toxicity, and solubility prediction tasks.

What is scaffold splitting and why use it for molecular property prediction?▼

Scaffold splitting is a dataset partitioning technique that groups molecules by structural scaffolds to reduce data leakage. It is essential for robust drug-discovery-style evaluation and ensures reliable model testing across novel chemical structures.

Can I apply transfer learning to small molecular datasets?▼

Yes, you can apply transfer learning to small molecular datasets by fine-tuning pretrained ChemBERTa, GROVER, or MolFormer-style pipelines. This approach improves prediction performance when adapting models to new scaffolds with limited data.

What featurization options are available for molecular ML models?▼

Available featurization options for molecular ML include generating fingerprints, descriptors, and graph features. These representations support both traditional ML algorithms and Graph Neural Networks for classification and regression tasks.

How do I evaluate models on MoleculeNet benchmarks?▼

To evaluate models on MoleculeNet benchmarks, you can use built-in data loaders and model evaluation APIs to train multitask regressors or classifiers. This allows end-to-end testing on standard datasets like Delaney for solubility.

Does deepchem work with SDF and FASTA sequence inputs?▼

Yes, deepchem supports ingesting SDF and FASTA sequence inputs alongside SMILES strings. You can build datasets from these formats to train models for various chemical and biological molecular property prediction tasks.

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