Issue No. 04 - July-Aug. (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.35
S. Bandyopadhyay , Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
S. Sengupta , Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
De novo ligand design involves optimization of several ligand properties such as binding affinity, ligand volume, drug likeness, etc. Therefore, optimization of these properties independently and simultaneously seems appropriate. In this paper, the ligand design problem is modeled in a multiobjective using Archived MultiObjective Simulated Annealing (AMOSA) as the underlying search algorithm. The multiple objectives considered are the energy components similarity to a known inhibitor and a novel drug likeliness measure based on Lipinski's rule of five. RecA protein of Mycobacterium tuberculosis, causative agent of tuberculosis, is taken as the target for the drug design. To gauge the goodness of the results, they are compared to the outputs of LigBuilder, NEWLEAD, and Variable genetic algorithm (VGA). The same problem has also been modeled using a well-established genetic algorithm-based multiobjective optimization technique, Nondominated Sorting Genetic Algorithm-II (NSGA-II), to find the efficacy of AMOSA through comparative analysis. Results demonstrate that while some small molecules designed by the proposed approach are remarkably similar to the known inhibitors of RecA, some new ones are discovered that may be potential candidates for novel lead molecules against tuberculosis.
simulated annealing, bioinformatics, diseases, drugs, genetic algorithms, medical computing, microorganisms, molecular biophysics, proteins, nondominated sorting genetic algorithm-II, RecA inhibitors, de novo ligand design, binding affinity, ligand volume, drug likeness, archived multiobjective simulated annealing, AMOSA, search algorithm, energy component similarity, Lipinski rule of five, RecA protein, Mycobacterium tuberculosis, drug design, LigBuilder, NEWLEAD, variable genetic algorithm, genetic algorithm-based multiobjective optimization, Drugs, Genetic algorithms, Proteins, Algorithm design and analysis, Simulated annealing, Inhibitors, rational drug design., De novo ligand design, multiobjective optimization, genetic algorithm, simulated annealing, oral bioavailability, Mycobacterium tuberculosis
S. Bandyopadhyay and S. Sengupta, "De Novo Design of Potential RecA Inhibitors Using MultiObjective Optimization," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1139-1154, 2012.