  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
novel Neighbourhood Based Genetic Algorithm (NBGA) which uses dynamic
neighbourhood topology. To get variable tree size, a variable-length version of
the above algorithm is devised. To judge the merit of the algorithm, it is
initially applied on the well known Travelling Salesman Problem (TSP).
