Convert SMILES strings and RDKit molecules into numerical feature vectors.
npx skills add https://github.com/hxk622/TokenDance --skill molfeat-hxk622
Please help me install this Agent Skill. Skill: molfeat Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/chemistry/molfeat Command: npx skills add https://github.com/hxk622/TokenDance --skill molfeat-hxk622
This Skill simplifies the complex process of converting molecular structures into numerical representations required for machine learning, enabling faster and more accurate cheminformatics and drug discovery.
Use the molfeat skill to convert the SMILES string 'CCO' into ECFP features.
High-intent search queries and answers about installing and using this skill.
You can convert SMILES strings to ECFP fingerprints by applying molecular featurization, which transforms chemical structures into ML-ready numerical vectors. This supports SMILES and RDKit molecules for QSAR modeling and virtual screening.
Molecular featurization for drug discovery is the process of converting molecular structures into numerical representations required for machine learning. It generates hand-crafted descriptors or pre-trained deep learning embeddings for cheminformatics analysis.
Yes, you can use pre-trained embeddings like ChemBERTa for QSAR modeling. Molecular featurization provides over 100 featurizers, including pre-trained deep learning embeddings like ChemBERTa and GIN, to predict drug candidate properties.
Yes, molecular featurization supports RDKit descriptors and Mordred. It offers a library of over 100 featurizers including hand-crafted molecular descriptors like RDKit and Mordred alongside fingerprints such as ECFP and MACCS.
The best way to generate numerical representations for virtual screening is using molecular featurization to convert SMILES strings into ML-ready feature vectors. This facilitates similarity searching by providing diverse featurizers like ECFP fingerprints.
No, you do not need RDKit molecules to perform molecular featurization, as it directly accepts SMILES strings as input. However, it also fully supports RDKit molecules for conversion into over 100 types of ML-ready feature vectors.
SologaConvert SMILES strings into over 100 molecular fingerprints and descriptors.
OwnLabAIConvert SMILES or RDKit molecules into fixed-length numerical vectors for ML tasks.
dp-archiveCalculate molecular descriptors and generate fingerprints from SMILES strings using RDKit.
sanand0Convert chemical structures into machine learning-ready numerical representations.
