Analyzing the performance of conformational search programs on compound databases

Loferer, Markus J.; Kolossváry, István; Aszódi, András

doi:10.1016/j.jmgm.2006.05.008

Cited by 17 publications

(27 citation statements)

References 21 publications

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“…The probability of a particular conformation being populated at room temperature is determined by its energy, according to the Boltzmann distribution. 1 Therefore, in numerous drug design applications such as Quantitative Structure Activity Relationship (QSAR), active analog approaches, and virtual screening of virtual libraries, a key step is conformational searching 2,3 to identify conformations with energies close to the global energy minimum.…”

Section: Introductionmentioning

confidence: 99%

Conformational searching using a population‐based incremental learning algorithm

Long

Tran²,

Adams

et al. 2011

J Comput Chem

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A new population-based incremental learning algorithm for conformational searching of molecules is presented. This algorithm is particularly effective at determining, by relatively small number of energy minimizations, global energy minima of large flexible molecules. The algorithm is also able to find a large set of low energy conformations of more rigid small molecules. The performance of the algorithm is relation to other algorithm is examined via the test molecules: C(18) H(38) , C(39)H(80) , cycloheptadecane and a set of five drug-like molecules.

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Section: Introductionmentioning

confidence: 99%

Conformational searching using a population‐based incremental learning algorithm

Long

Tran²,

Adams

et al. 2011

J Comput Chem

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“…the generation of a set of conformers for each molecule. A number of different tools capable of generating multiple conformers already exist [9,10]. A possible workflow using the OMEGA (OpenEye Scientific Software, USA) and CORINA (Molecular Networks GmbH, Germany) tools as precomputing step is illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Pharao: Pharmacophore alignment and optimization

Taminau¹,

Thijs²,

Winter³

2008

Journal of Molecular Graphics and Modelling

113

129

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“…Although structural information for the biopolymer target may still be unavailable, structural models can be readily obtained for the small compounds of interest [1,2]. Nowadays such models tend to be multiconformer ensemble generated computationally [3][4][5][6][7][8][9]. Such conformational models can be used for virtual screening or to direct synthetic chemistry, even in absence of a structure for the biological target (in particular for membrane bound proteins).…”

Section: Introductionmentioning

confidence: 99%

“…For instance: i) knowledge-based methods can call upon an expanding database of experimental structures [56,[68][69][70], ii) the influence of solvation may be included, explicitly [71] or via continuum methods [72,73], iii) quantum-mechanical calculations have become more tractable [74], and iv) conformational coverage can be extended and compared across methods [7][8][9]. In addition, the methods have become easier to use thanks to better graphical user interfaces and pipelining tools [75].…”

Section: Introductionmentioning

confidence: 99%

Conformational Sampling and Energetics of Drug-Like Molecules

Foloppe

Chen²

2009

CMC

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The pharmacological properties of small organic molecules depend on their three-dimensional (3D) structure. That includes physico-chemical properties (e.g. solubility, partition equilibria) and molecular recognition such as binding to a therapeutic macromolecular target. At physiological temperature, the 3D structure of a flexible small molecules is expected to cover an ensemble of energetically accessible conformations. Therefore, it is of fundamental and practical importance to be able to relate the energetics of a molecule to its conformational preferences and derived properties, a discipline known as conformational analysis. The first step of conformational analysis is the generation of the conformers, referred to as conformational sampling. This is typically performed primarily using computational chemistry methods. Taking a fresh look at these methods for a broad medicinal chemistry audience is the object of the present review. Indeed, conformational sampling methods continue to be developed, improved and tested. They underpin much of the detailed analysis of structure-activity relationships on selected chemical series, but also the preparation of large conformational libraries of generic compounds and their exploitation for virtual screening. In recent years, the conformational models of active compounds have been examined to see how frequently they capture their target-bound bioactive conformation, as revealed by X-ray crystallography. This provided a context to scrutinize the intrinsic conformational energetics of these bioactive conformers, but this subject is still intensely debated. Another line of investigation concerns the conformational diversity of the 3D models, and how well they cover the conformational and pharmacophoric spaces. This review addresses in general terms: i) the basic principles of conformational analysis, including modern computational estimates of intramolecular energy and how those are mapped on the molecular potential energy surface, ii) some experimental contributions to probing of the small molecule conformations, iii) the various computational methods available to generate conformational models, iv) the conformational properties of the bioactive conformers, and v) attempts to quantify the coverage of the conformational models and the controlling parameters.

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Analyzing the performance of conformational search programs on compound databases

Cited by 17 publications

References 21 publications

Conformational searching using a population‐based incremental learning algorithm

Conformational searching using a population‐based incremental learning algorithm

Pharao: Pharmacophore alignment and optimization

Conformational Sampling and Energetics of Drug-Like Molecules

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