Recent advances in<i>de novo</i>design strategy for practical lead identification

Honma, Teruki

doi:10.1002/med.10046

Cited by 58 publications

(17 citation statements)

References 79 publications

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“…ZINC, http://zinc.docking.org/choose.shtml (11)]. Thus, it is possible to select a small- and focused-library of available analogs that can be further evaluated by a virtual screening step and eventually tested experimentally (12). Likewise, de novo drug design approach has the property to suggest new scaffold candidates.…”

Section: Introductionmentioning

confidence: 99%

e-LEA3D: a computational-aided drug design web server

Douguet¹

2010

Nucleic Acids Research

122

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e-LEA3D web server integrates three complementary tools to perform computer-aided drug design based on molecular fragments. In drug discovery projects, there is a considerable interest in identifying novel and diverse molecular scaffolds to enhance chances of success. The de novo drug design tool is used to invent new ligands to optimize a user-specified scoring function. The composite scoring function includes both structure- and ligand-based evaluations. The de novo approach is an alternative to a blind virtual screening of large compound collections. A heuristic based on a genetic algorithm rapidly finds which fragments or combination of fragments fit a QSAR model or the binding site of a protein. While the approach is ideally suited for scaffold-hopping, this module also allows a scan for possible substituents to a user-specified scaffold. The second tool offers a traditional virtual screening and filtering of an uploaded library of compounds. The third module addresses the combinatorial library design that is based on a user-drawn scaffold and reactants coming, for example, from a chemical supplier. The e-LEA3D server is available at: http://bioinfo.ipmc.cnrs.fr/lea.html.

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Section: Introductionmentioning

confidence: 99%

e-LEA3D: a computational-aided drug design web server

Douguet¹

2010

Nucleic Acids Research

122

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“…Nonetheless, the crystal structures of other homologous CDKs in non‐ligand‐bound and ligand‐bound forms have provided much information pertinent to the catalytic mechanism and rational design of inhibitor drugs for CDK4 24–28. Indeed, a structure‐based drug design campaign has proved successful in designing new selective CDK4 inhibitors through the use of the known structural information for CDK2 and comparison of the active site amino acid sequences of CDK2 and CDK4 29–32…”

Section: Introductionmentioning

confidence: 99%

Loop Flexibility and Solvent Dynamics as Determinants for the Selective Inhibition of Cyclin‐Dependent Kinase 4: Comparative Molecular Dynamics Simulation Studies of CDK2 and CDK4

2004

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The design and discovery of selective cyclin-dependent kinase 4 (CDK4) inhibitors have been actively pursued in order to develop therapeutic cancer treatments. By means of a consecutive computational protocol involving homology modeling, docking experiments, and molecular dynamics simulations, we examine the characteristic structural and dynamic properties that distinguish CDK4 from CDK2 in its complexation with selective inhibitors. The results for all three CDK4-selective inhibitors under investigation show that the large-amplitude motion of a disordered loop of CDK4 is damped out in the presence of the inhibitors whereas their binding in the CDK2 active site has little effect on the loop flexibility. It is also found that the binding preference of CDK4- selective inhibitors for CDK4 over CDK2 stems from the reduced solvent accessibility in the active site of the former due to the formation of a stable hydrogen-bond triad by the Asp99, Arg101, and Thr102 side chains at the top of the active-site gorge. Besides the differences in loop flexibility and solvent accessibility, the dynamic stabilities of the hydrogen bonds between the inhibitors and the side chain of the lysine residue at the bottom of the active site also correlate well with the relative binding affinities of the inhibitors for the two CDKs. These results highlight the usefulness of this computational approach in evaluating the selectivity of a CDK inhibitor, and demonstrate the necessity of considering protein flexibility and solvent effects in designing new selective CDK4-selective inhibitors.

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“…Our first example of applying entropy for inductive inference is in silico drug discovery. Virtual screening has attracted much attention in the pharmaceutical industry [ 12 , 13 ]. It provides a more economical way to screen diverse chemicals as drug candidates compared with a wet-lab approach.…”

Section: Maximum Entropy In Biology and Drug Discoverymentioning

confidence: 99%

Mathematical and computational modeling in biology at multiple scales

Tuszyński

Winter

White

et al. 2014

Theor Biol Med Model

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A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. The utility of quantum mechanical methods in biophysical and biochemical modeling is explored. The field of computational enzymology is examined.

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Recent advances inde novodesign strategy for practical lead identification

Cited by 58 publications

References 79 publications

e-LEA3D: a computational-aided drug design web server

e-LEA3D: a computational-aided drug design web server

Loop Flexibility and Solvent Dynamics as Determinants for the Selective Inhibition of Cyclin‐Dependent Kinase 4: Comparative Molecular Dynamics Simulation Studies of CDK2 and CDK4

Mathematical and computational modeling in biology at multiple scales

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