An Evolutionary Approach to Drug-Design Using Quantam Binary Particle Swarm Optimization Algorithm

TL;DR

This paper introduces a novel quantum binary particle swarm optimization algorithm to evolve ligand structures for drug design, optimizing their interaction energy with target proteins using variable and fixed tree representations.

Contribution

It presents a new evolutionary approach employing quantum discrete PSO with variable tree sizes for ligand design, improving upon fixed-length methods.

Findings

Variable tree size configuration outperforms fixed length in ligand optimization.

Quantum binary PSO effectively minimizes ligand-protein interaction energy.

The approach adapts to different active site configurations.

Abstract

The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein. The structure is represented as a tree where each non-empty node represents a functional group. It is assumed that the active site configuration of the target protein is known with position of the essential residues. In this paper the interaction energy of the ligands with the protein target is minimized. Moreover, the size of the tree is difficult to obtain and it will be different for different active sites. To overcome the difficulty, a variable tree size configuration is used for designing ligands. The optimization is done using a quantum discrete PSO. The result using fixed length and variable length configuration are compared.