Does this support transfer learning for small datasets?

Yes, it provides implementations for fine-tuning pretrained models such as ChemBERTa, GROVER, and MolFormer to improve performance when data is limited.

Can this skill handle 3D molecular structures?

Yes, the skill includes guidance on using 3D featurizers like Coulomb Matrix and models suitable for spatial chemical data and materials science.

Why is ScaffoldSplitter recommended over random splitting?

In drug discovery, random splitting can lead to data leakage if similar molecular scaffolds appear in both training and test sets. ScaffoldSplitter ensures models are tested on truly novel structures.

What is DeepChem and how does this skill use it?

DeepChem is a Python library for applying ML to chemistry and biology. This skill provides Claude with the patterns and best practices needed to implement DeepChem workflows for molecular property prediction and data processing.

What types of chemical data can I process?

The skill supports SMILES strings, SDF files, FASTA sequences, and common tabular data formats used in bioinformatics and cheminformatics.

DeepChem Molecular ML

Name: DeepChem Molecular ML
Author: Microck

byMicrock

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데이터 과학 및 ML

Facilitates molecular machine learning, property prediction, and drug discovery workflows using the DeepChem library.

소개

DeepChem is a specialized Python library designed to democratize machine learning for chemistry, materials science, and biology. This skill enables Claude to perform complex tasks such as featurizing SMILES strings, implementing Graph Neural Networks (GNNs) for property prediction, and utilizing MoleculeNet benchmarks. It provides domain-specific guidance on crucial steps like ScaffoldSplitter to prevent data leakage and the application of pretrained models like ChemBERTa for transfer learning, making it an essential tool for AI-driven drug discovery and chemical informatics.

주요 기능

Scaffold-based data splitting for robust model validation in drug discovery
Pre-configured Graph Neural Network implementations (GCN, GAT, MPNN, AttentiveFP)
Specialized data loading for chemical formats including SMILES, SDF, and FASTA
Comprehensive molecular featurization (fingerprints, graph representations, and descriptors)
81 GitHub stars
Access to MoleculeNet benchmark datasets for standardized model evaluation

사용 사례

Analyzing protein and DNA sequences for functional prediction tasks
Developing graph-based models for materials science and crystal property analysis
Predicting ADMET and toxicity properties for potential drug candidates

소개

주요 기능

Scaffold-based data splitting for robust model validation in drug discovery
Pre-configured Graph Neural Network implementations (GCN, GAT, MPNN, AttentiveFP)
Specialized data loading for chemical formats including SMILES, SDF, and FASTA
Comprehensive molecular featurization (fingerprints, graph representations, and descriptors)
81 GitHub stars
Access to MoleculeNet benchmark datasets for standardized model evaluation

사용 사례

Analyzing protein and DNA sequences for functional prediction tasks
Developing graph-based models for materials science and crystal property analysis
Predicting ADMET and toxicity properties for potential drug candidates