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A grid algorithm for high throughput fitting of dose-response curve data.

Yuhong Wang¹, Ajit Jadhav, Noel Southal

¹National Institutes of Health, NIH Chemical Genomics Center, 9800 Medical Center Drive, Rockville, MD 20850, USA.

Current Chemical Genomics

|February 19, 2011

Summary

This summary is machine-generated.

A novel Grid algorithm and program efficiently fit dose-response curves, outperforming existing methods, especially with noisy data. This tool enhances high-throughput screening and drug discovery analysis.

Keywords:

Curve fitting Dose-response curve Grid algorithm High throughput screening.Hill equation

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Comprehensive Analysis of Drug Response using the FLICK Assay

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Published on: June 6, 2025

Area of Science:

Computational Biology
Pharmacology
Bioinformatics

Background:

Accurate dose-response curve fitting is crucial for drug discovery.
Existing methods struggle with noisy or incomplete data in high-throughput screening.
The four-parameter symmetric logistic model is widely used.

Purpose of the Study:

Introduce a novel Grid algorithm and computer program for dose-response curve fitting.
Evaluate the Grid program's performance against established tools (DRC, XLfit).
Assess robustness and scalability for high-throughput analysis.

Main Methods:

Developed the Grid algorithm, a four-dimensional parameter search method.
Utilized simulated dose-response curves with varying noise levels.
Compared Grid program performance with R's DRC package and Microsoft Excel's XLfit.

Main Results:

The Grid program demonstrated robustness, accurately recovering parameters from simulated data.
Grid consistently performed well with complete, partial, and noisy curves.
DRC and XLfit showed degraded performance with increasing data noise.

Conclusions:

The Grid program offers a scalable and automated solution for high-throughput dose-response data analysis.
It significantly improves productivity in early drug discovery.
Applicable to diverse curve-fitting challenges in chemical, biological, and medical sciences.