Conformation-Importance-ML-Models
importedtherapeutics/conformation-importance-ml-models
Official repository for the paper "Understanding Conformation Importance in Data-driven Property Prediction Models"
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Therapeutics
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- maintained
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Documentation
- unknown
- Tags
- chemoinformatics · deep-learning · drug-discovery · machinelearning · molecular-descriptors
- Regulatory
- unknown
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- Rcpidrug-discovery · molecular-descriptors
💊 Molecular informatics toolkit with integration of bioinformatics and cheminformatics tools for drug discovery
- rdkit-clidrug-discovery · molecular-descriptors
Python command line tool for rdkit library, fast, simple and reactive
- ILThermoPymolecular-descriptors
Unofficial Python client for ILThermo 2.0 with SMILES-enriched ionic-liquid data for cheminformatics, thermodynamics, and ML workflows.
- qedmolecular-descriptors
The QED (Quantitative Estimation of Drug-likeness) molecular descriptor to use in combination with RDKit
- cshl-singlecell-2017machinelearning
Single Cell Analysis course at Cold Spring Harbor Laboratory 2017
- MindCare-AImachinelearning
MindCare AI is a smart tool that uses machine learning to help find mental health issues. It looks at how people behave to spot signs of problems. This tool helps doctors and health workers make…
- api.github.com/repos/YuHamakawa/Conformation-Importance-ML-Modelsretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2025-07-16, 3 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/48.json→ .entries["conformation-importance-ml-models"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.