• The Plant Pathology Journal
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• v.33 no.6
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• pp.529-542
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• 2017

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.

• Genomics & Informatics
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• v.5 no.1
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• pp.24-29
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• 2007

Computational virtual screening has become an essential platform of drug discovery for the efficient identification of active candidates. Moleculardocking, a key technology of receptor-centric virtual screening, is commonly used to predict the binding affinities of chemical compounds on target receptors. Despite the advancement and extensive application of these methods, substantial improvement is still required to increase their accuracy and time-efficiency. Here, we evaluate several advanced structure-based virtual screening approaches for elucidating the rank-order activity of chemical libraries, and the quantitative structureactivity relationship (QSAR). Our results show that the ensemble-average free energy estimation, including implicit solvation energy terms, significantly improves the hit enrichment of the virtual screening. We also demonstrate that the assignment of quantum mechanical-polarized (QM-polarized) partial charges to docked ligands contributes to the reproduction of the crystal pose of ligands in the docking and scoring procedure.

• Journal of Integrative Natural Science
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• v.7 no.2
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• pp.75-78
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• 2014

Computer-aided drug design uses computational chemistry to discover, enhance, or study drugs and related biologically active molecules. It is now proved to be effective in reducing costs and speeding up drug discovery. In this short review, we discussed on the importance of combining molecular docking and molecular dynamics simulation methodologies. We also reviewed the importance of protein flexibility, refinement of docked complexes using molecular dynamics and the use of free energy calculations for the calculation of accurate binding energies has been reviewed.

• Proceedings of the PSK Conference
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• 2003.10a
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• pp.91-91
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• 2003

The beta-turn has been implicated as an important conformation for biological recognition of peptides or proteins. We adapted the concept of general Ca atom positioning from the cluster analysis and recombination of each ideal beta-turn conformation pattern by Garland and Dean (1. Computer-Aided Molecular Design, 1999, 13, 469) as one strategy of designing non-peptide beta-turn scaffolds. (omitted)

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.2291-2295
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• 2016

Small molecule tyrosine kinase inhibitors targeting HER 2 receptors have emerged as an important therapeutic approach in inhibition of downstream proliferation and survival signals for the treatment of breast cancers. Recent drug discovery efforts have demonstrated that naturally occurring polyphenolic compounds like delphinidin have potential to inhibit proliferation and promote apoptosis of breast cancer cells by targeting HER2 receptors. While delphinidin may thus reduce tumour size, it is associated with serious side effects like dysphonia. Owing to the narrow therapeutic window of delphinidin, the present study aimed to identify high affinity compounds targeting HER2 with safer pharmacological profiles than delphinidin through virtual screening approaches. Delphinidin served as the query parent for identification of structurally similar compounds by Tanimoto-based similarity searching with a threshold of 95% against the PubChem database. The compounds retrieved were further subjected to Lipinski and Verber's filters to obtain drug like agents, then further filtered by diversity based screens with a cut off of 0.6. The compound with Pubchem ID: 91596862 was identified to have higher affinity than its parent. In addition it also proved to be non-toxic with a better ADMET profile and higher kinase activity. The compound identified in the study can be put to further in vitro drug testing to complement the present study.

• Bulletin of the Korean Chemical Society
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• v.29 no.5
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• pp.921-927
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• 2008

Discovery of $\alpha$-glucosidase inhibitors has been actively pursued with the aim to develop therapeutics for the treatment of diabetes and the other carbohydrate mediated diseases. As a method for the discovery of new novel inhibitors of $\alpha$-glucosidase, we have addressed the performance of the computer-aided drug design protocol involving the homology modeling of $\alpha$-glucosidase and the structure-based virtual screening with the two docking tools: FlexX and the automated and improved AutoDock implementing the effects of ligand solvation in the scoring function. The homology modeling of $\alpha$-glucosidase from baker’s yeast provides a high-quality 3-D structure enabling the structure-based inhibitor design. Of the two docking programs under consideration, AutoDock is found to be more accurate than FlexX in terms of scoring putative ligands to the extent of 5-fold enhancement of hit rate in database screening when 1% of database coverage is used as a cutoff. A detailed binding mode analysis of the known inhibitors shows that they can be stabilized in the active site of $\alpha$- glucosidase through the simultaneous establishment of the multiple hydrogen bond and hydrophobic interactions. The present study demonstrates the usefulness of the automated AutoDock program with the improved scoring function as a docking tool for virtual screening of new $\alpha$-glucosidase inhibitors as well as for binding mode analysis to elucidate the activities of known inhibitors.