Evolving fragments to lead molecules

This article describes a variable string length genetic algorithm for de novo ligand design. The input to the algorithm is the active site dimensions which guides the ligand construction. A library of forty one fragments is used to construct the ligands by evaluating the combinations of these fragments. Bond stretching, angle bending, torsional terms, van der Waals and electrostatic interaction energy with distance dependent dielectric constant contribute are used to evaluate the internal energy of the ligand and the interaction energy of the ligand receptor complex. Domain specific genetic operators are used to evolve the solutions to obtain better ligands. Experimental results for HIV-1 Protease and Thrombin are provided which underline the superiority of the proposed scheme over three existing approaches.

[1]  Ujjwal Maulik,et al.  Active Site Driven Ligand Design: an Evolutionary Approach , 2005, J. Bioinform. Comput. Biol..

[2]  Luhua Lai,et al.  LigBuilder: A Multi-Purpose Program for Structure-Based Drug Design , 2000 .

[3]  A Itai,et al.  Confirmation of usefulness of a structure construction program based on three-dimensional receptor structure for rational lead generation. , 1993, Journal of medicinal chemistry.

[4]  James A. Foster,et al.  Evolving Molecules for Drug Design Using Genetic Algorithms via Molecular Trees , 2000, GECCO.

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Cheng-Yan Kao,et al.  Flexible ligand docking using a robust evolutionary algorithm , 2000, J. Comput. Chem..

[7]  A. Leach.  Molecular Modelling: Principles and Applications , 1996 .

[8]  Robin Taylor,et al.  New software for searching the Cambridge Structural Database and visualizing crystal structures. , 2002, Acta crystallographica. Section B, Structural science.

[9]  N. Cohen,et al.  The NEWLEAD Program: A New Method for the Design of Candidate Structures from Pharmacophoric Hypotheses. , 1994 .

[10]  Regine Bohacek,et al.  Multiple Highly Diverse Structures Complementary to Enzyme Binding Sites: Results of Extensive Application of a de Novo Design Method Incorporating Combinatorial Growth , 1994 .

[11]  Nicolas Majeux,et al.  Structure‐based ligand design by a build‐up approach and genetic algorithm search in conformational space , 2001, J. Comput. Chem..

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  D. Banner,et al.  Crystallographic analysis at 3.0-A resolution of the binding to human thrombin of four active site-directed inhibitors. , 1994, The Journal of biological chemistry.

[14]  Irwin D. Kuntz,et al.  A genetic algorithm for structure-based de novo design , 2001, J. Comput. Aided Mol. Des..

[15]  A. Globus,et al.  Automatic molecular design using evolutionary techniques , 1999 .

[16]  A M Hassell,et al.  Hydroxyethylene isostere inhibitors of human immunodeficiency virus-1 protease: structure-activity analysis using enzyme kinetics, X-ray crystallography, and infected T-cell assays. , 1992, Biochemistry.