LigEGFR: Spatial graph embedding and molecular descriptors assisted bioactivity prediction of ligand molecules for epidermal growth factor receptor on a cell line-based dataset
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Updated
Dec 29, 2020
LigEGFR: Spatial graph embedding and molecular descriptors assisted bioactivity prediction of ligand molecules for epidermal growth factor receptor on a cell line-based dataset
Visualizing the Equilibrium and Kinetics of Protein-Ligand Binding and Competitive Binding
Machine learning pipeline using RDKit molecular fingerprints, LightGBM, and SHAP explainability to predict small-molecule IC50 bioactivity.
Open-source Python toolkit for reproducible enzyme kinetics, Michaelis-Menten fitting, and IC50 dose-response analysis.
A Python script predicting pIC50 values of compounds obtained from the ChEMBL database against the target protein, aromatase. The molecular descriptor used in this script is PubChem fingerprint.
Dose-response curves in Python.
Reproducible dose-response analysis for anthelmintic resistance assays
Dose-response and IC50 analysis app with model comparison and publication-ready plots
Dose-response curve fitting that extracts IC50, the headline number of every drug-screening experiment.
Dose-response analysis and IC50 determination (4PL curve fitting, Z'-factor assay QC) in Python and R.
4-parameter logistic curve fitting for dose-response analysis using NumPy and SciPy
This application processes and analyzes data from high throughput luciferase-based virus neutralization assays.
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