Linear Regression of Kinetic Data with Chemical Descriptors

Linear regression is a statistical method used for modeling a relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. When only one explanatory variable is involved, it is called simple linear regression. And in case of many explanatory variabes, the process is termed multiple linear regression.

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

For examples of previously performed studies in which Linear Regression of Kinetic Data with Chemical Descriptors was the primary method used, see the following example cases:

• In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method.
• Protein-ligand interaction fingerprints for accurate prediction of dissociation rates of p38 MAPK Type II inhibitors.
• Ligand Desolvation Steers On-Rate and Impacts Drug Residence Time of Heat Shock Protein 90 (Hsp90) Inhibitors.
• Discovery of 7-Oxo-2,4,5,7-tetrahydro-6 H-pyrazolo[3,4- c]pyridine Derivatives as Potent, Orally Available, and Brain-Penetrating Receptor Interacting Protein 1 (RIP1) Kinase Inhibitors: Analysis of Structure-Kinetic Relationships.
• Constructing Interconsistent, Reasonable, and Predictive Models for Both the Kinetic and Thermodynamic Properties of HIV-1 Protease Inhibitors.
• Multiple Linear Regression of kinetic dataset of MAP38 kinase inhibitors using chemical descriptors

Linear Regression of Kinetic Data with Chemical Descriptors was also used in the following examples:

• Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times
• Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics.