The Effects of Biasing Torsional Mutations in a Conformational GA

Strizhev, Alex; Abrahamian, Edmond J.; Choi, Sun; Leonard, Joseph M.; Wolohan, Philippa R. N.; Clark, Robert D.

doi:10.1021/ci0502193

Cited by 5 publications

(19 citation statements)

References 15 publications

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“…41 Torsion angle values that result in low-energy geometries were identified based on a set of random conformations generated in a preprocessing step. Torsion angle mutations were then biased toward these favorable values.…”

Section: Generation Of Template Conformationmentioning

confidence: 99%

Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm

Vainio

Johnson

2007

J. Chem. Inf. Model.

355

304

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The task of generating a nonredundant set of low-energy conformations for small molecules is of fundamental importance for many molecular modeling and drug-design methodologies. Several approaches to conformer generation have been published. Exhaustive searches suffer from the exponential growth of the search space with increasing degrees of conformational freedom (number of rotatable bonds). Stochastic algorithms do not suffer as much from the exponential increase of search space and provide a good coverage of the energy minima. Here, the use of a multiobjective genetic algorithm in the generation of conformer ensembles is investigated. Distance geometry is used to generate an initial conformer, which is then subject to geometric modifications encoded by the individuals of the genetic algorithm. The geometric modifications apply to torsion angles about rotatable bonds, stereochemistry of double bonds and tetrahedral chiral centers, and ring conformations. The geometric diversity of the evolving conformer ensemble is preserved by a fitness-sharing mechanism based on the root-mean-square distance of the atomic coordinates. Molecular symmetry is taken into account in the distance calculation. The geometric modifications introduce strain into the structures. The strain is relaxed using an MMFF94-like force field in a postprocessing step that also removes conformational duplicates and structures whose strain energy remains above a predefined window from the minimum energy value found in the set. The implementation, called Balloon, is available free of charge on the Internet ( http://www.abo.fi/~mivainio/balloon/).

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Section: Generation Of Template Conformationmentioning

confidence: 99%

Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm

Vainio

Johnson

2007

J. Chem. Inf. Model.

355

304

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show abstract

“…(a) Twelve conformers generated using CONFORT. (b) Twelve conformers obtained by randomizing torsions, then relieving ''bumps'' using the directed tweak algorithm [22]. (c) The first four conformers generated by KBConf.…”

Section: Resultsmentioning

confidence: 99%

“…9. The top row shows two conformational ensembles for an endothelin antagonist [42]: a dozen produced by CONFORT and another dozen produced by randomization followed by bump relief using directed tweak [22]. The remaining four overlays in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Having shown that piece-wise sampling of local torsional minima explores conformational space more efficiently than does uniform random sampling [22], we wanted to extend the methodology to better sample broad minima. Doing so led to the development of a two-stage process.…”

Section: Introductionmentioning

confidence: 99%

“…Application to the smaller test set compiled by Boström [21] made it possible to compare the performance of KBConf to that of other conformational generators. We also discuss the qualitative differences between our approach, CON-FORT and a torsional bias model described earlier [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A knowledge-based approach to generating diverse but energetically representative ensembles of ligand conformers

Dorfman¹,

Smith²,

Masek³

et al. 2007

J Comput Aided Mol Des

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This paper describes a new and efficient stochastic conformational sampling method for generating a range of low-energy molecule conformations. Sampling can be tailored to a specific structural domain (e.g., peptides) by extracting torsional profiles from specific datasets and subsequently applying them to target molecules outside the reference set. The programs that handle creation of the knowledge-based torsional profiles and conformer generation per se are separate and so can be used independently or sequentially, depending on the task at hand. The conformational ensembles produced are contrasted with those generated using local minimization approaches. They are also quantitatively compared with a broader range of techniques in terms of speed and the ability to reproduce bound ligand conformations found in complexes with proteins.

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Conformation Generation: The State of the Art

Hawkins¹

2017

J. Chem. Inf. Model.

200

214

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The generation of conformations for small molecules is a problem of continuing interest in cheminformatics and computational drug discovery. This review will present an overview of methods used to sample conformational space, focusing on those methods designed for organic molecules commonly of interest in drug discovery. Different approaches to both the sampling of conformational space and the scoring of conformational stability will be compared and contrasted, with an emphasis on those methods suitable for conformer sampling of large numbers of drug-like molecules. Particular attention will be devoted to the appropriate utilization of information from experimental solid-state structures in validating and evaluating the performance of these tools. The review will conclude with some areas worthy of further investigation.

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The Effects of Biasing Torsional Mutations in a Conformational GA

Cited by 5 publications

References 15 publications

Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm

Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm

A knowledge-based approach to generating diverse but energetically representative ensembles of ligand conformers

Conformation Generation: The State of the Art

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