Receptor‐specific scoring functions derived from quantum chemical models improve affinity estimates for <i>in‐silico</i> drug discovery

Fischer, Bernhard; Fukuzawa, Kaori; Wenzel, Wolfgang

doi:10.1002/prot.21607

Cited by 40 publications

(28 citation statements)

References 84 publications

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“…These individual potential terms can be combined into forcefields, some of which can already be selected in the graphical user interface (GUI), like protein forcefields PFF01/02[46] and the potential used in FlexScreen for receptor‐ligand docking. [47] The parameterizations of some of these forcefields exist for proteins, DNA and can be adapted using the atom‐type based assignment of parameters to small molecules. For parameterization of nonstandard systems, a parameter generator is supplied in the GUI, which can import partial charges from density functional theory (DFT) calculations and supply defaults for Van‐der‐Waals radii (which can be modified by the user).…”

Section: Methodsmentioning

confidence: 99%

SIMONA 1.0: An efficient and versatile framework for stochastic simulations of molecular and nanoscale systems

Strunk¹,

Wolf²,

Brieg³

et al. 2012

J Comput Chem

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Molecular simulation methods have increasingly contributed to our understanding of molecular and nanoscale systems. However, the family of Monte Carlo techniques has taken a backseat to molecular dynamics based methods, which is also reflected in the number of available simulation packages. Here, we report the development of a generic, versatile simulation package for stochastic simulations and demonstrate its application to protein conformational change, protein-protein association, small-molecule protein docking, and simulation of the growth of nanoscale clusters of organic molecules. Simulation of molecular and nanoscale systems (SIMONA) is easy to use for standard simulations via a graphical user interface and highly parallel both via MPI and the use of graphical processors. It is also extendable to many additional simulations types. Being freely available to academic users, we hope it will enable a large community of researchers in the life- and materials-sciences to use and extend SIMONA in the future. SIMONA is available for download under http://int.kit.edu/nanosim/simona.

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Section: Methodsmentioning

confidence: 99%

SIMONA 1.0: An efficient and versatile framework for stochastic simulations of molecular and nanoscale systems

Strunk¹,

Wolf²,

Brieg³

et al. 2012

J Comput Chem

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show abstract

“…It is used in the exploration of the binding space of the ligand and also in the evaluation of target–ligand complexes in molecular docking. The scoring functions can be categorized into knowledge-based [102–103], force-field based [104–105], empirical [106–107] and consensus [108–109]. These will be discussed below.…”

Section: Reviewmentioning

confidence: 99%

Computational methods in drug discovery

Leelananda

Lindert

2016

Beilstein J. Org. Chem.

502

309

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The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

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“…Fischer et al 555 improved the scoring functions representing the binding energy for human estrogen receptor subtype R and human retinoic acid receptor of isotype γ, using atomic charges from FMO calculations at the RHF/STO-3G level and concluded that such quantum scoring functions (QSF) describe the electrostatics accurately, and that QSF performs better than force field analogues. Yoshida et al 556 applied FMO/RHF/6-31G to QSAR studies of the binding affinity of substituted benzenesulfonamides with carbonic anhydrase, noting the difficulties in modeling Zn 2+ -containing systems.…”

Section: δEmentioning

confidence: 99%

Fragmentation Methods: A Route to Accurate Calculations on Large Systems

et al. 2011

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Theoretical chemists have always strived to perform quantum mechanics (QM) calculations on larger and larger molecules and molecular systems, as well as condensed phase species, that are frequently much larger than the current state-of-the-art would suggest is possible. The desire to study species (with acceptable accuracy) that are larger than appears to be feasible has naturally led to the development of novel methods, including semiempirical approaches, reduced scaling methods, and fragmentation methods. The focus of the present review is on fragmentation methods, in which a large molecule or molecular system is made more computationally tractable by explicitly considering only one part (fragment) of the whole in any particular calculation. If one can divide a species of interest into fragments, employ some level of ab initio QM to calculate the wave function, energy, and properties of each fragment, and then combine the results from the fragment calculations to predict the same properties for the whole, the possibility exists that the accuracy of the outcome can approach that which would be obtained from a full (nonfragmented) calculation. It is this goal that drives the development of fragmentation methods. Disciplines Chemistry CommentsReprinted (adapted) with permission from Chemical Reviews 112 (2012): 632,

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Receptor‐specific scoring functions derived from quantum chemical models improve affinity estimates for in‐silico drug discovery

Cited by 40 publications

References 84 publications

SIMONA 1.0: An efficient and versatile framework for stochastic simulations of molecular and nanoscale systems

SIMONA 1.0: An efficient and versatile framework for stochastic simulations of molecular and nanoscale systems

Computational methods in drug discovery

Fragmentation Methods: A Route to Accurate Calculations on Large Systems

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