Prediction of Binding Affinities for Protein-Ligand Complexes with Neural Network Models

Abstract

Molecular mechanisms of drug action are often based on an interaction of them with target macromolecules, such as proteins and nucleonic acids. The formation of ligandtarget complexes is typical for biologically active compounds, including activators and inhibitors of various enzymes. Prediction of the dissociation constant (Kd) of protein-ligand complex is often used as a scoring function for the modeled complexes and there are many approaches in the field of prediction of such constant [1]. In the present work various parameters of protein-ligand complexes were used to predict Kd. These parameters can be quickly calculated immediately during docking procedure, which we usually used for complexes modeling. The artificial feedforward neural networks (AFNNs) were used as a mathematical approach to prediction of protein-ligand complexes Kd. In practice, neural networks are especially useful for classification and function approximation problems, which have a lot of training data. Neural networks are often used in situations where you do not have enough prior knowledge to set the activation function, as in case of the prediction of the proteinligand complexes dissociation constant.