![]() ![]() (ML) techniques such as genetic algorithms, 31, 32 artificial neural network, 33, 34 Gaussian process regression In recent years, artificial intelligence (AI) and machine learning Level, and many structures need to be optimized at a higher level. Of configurational space has to be sampled at a lower accuracy method To avoid missing the true low-energy conformers, a large portion Methods at different levels predict different To refine the conformer structures and energies (such as force fields Promising candidate structuresĪre then funneled through more costly methods with higher accuracy ![]() Fast computational methods with lower accuracy are employed To balance efficiency and accuracy, hierarchical methods have beenĭeveloped. Provide energies for all configurations produced in the search. More often than not, quantum chemistry methods are too expensive to However, they are computationally costly. Higher accuracy in the estimation of molecular energies than forceįields because they describe the interactions and polarization in Quantum chemistry methods such as the density functional theory (DFT)Īnd coupled cluster (CC) theory. Two classes of totalĮnergy approaches are commonly used: force field-based methods and Systematic or stochastic algorithms mentioned before.Ĭhallenge in conformer searches is the sufficientlyĪccurate mapping of energies and structures. Knowledge-based methods usually need to be combined with the different Structure Database (CSD) 26 or the Proteinĭata Bank (PDB). The library is typicallyīased on experimental structures in databases such as the Cambridge Theyįor torsion angles and ring conformations. Sampling is required, and the results may be affected by the randomĭeveloped 24, 25 to achieve more consistent results. 11, 23 Stochastic methods can be applied to larger molecules with high-dimensionalĬonformer spaces, but the predicted conformers may vary. Restricted to predefined, most relevant ranges) based on differentĪlgorithms such as Monte Carlo annealing, 17, 18 minima hopping, 19 basin hopping, 20, 21 distance geometry, 22 and genetic algorithms. Methods randomly sample the phase space of torsion angles (sometimes Numbers of relevant torsion angles, i.e., search dimensions. Limited to small molecules because it scales poorly with increasing 11− 16 These methods can be broadly classified to be either systematicĪ systematic method relies on a grid to sample ![]() 1 A variety of methods and tools have been developed to generate diverseĬonformer structures. While keeping bond length and angles fixed. Most search methods focus on sampling the torsion angles in molecules Since theīond lengths and angles are relatively rigid in molecules and theĭifferent conformers originate from the flexible rotational groups, Is commonly applied to make the problem more tractable. 9, 10 For this reason, dimensionality reduction The conformational space (bond lengths,īond angles, and torsions) for even relatively small molecules isĮnormous. One of the persistent challenges in molecular modeling. Of local minima in typical energy landscapes make conformer searches Large size of configurational phase space and the considerable number 8 While one configuration of a small molecule canīe simulated routinely by ab initio methods, the Topic of great interest in computational chemistry, 4 cheminformatics, 5, 6 computational drug design, 7 and structure-based virtual screening. 1− 3 Therefore, identifying the low-energyĬonformers and determining their energy ranking continues to be a To a minimum on the molecule’s potential energy surface (PES).Īny molecule with rotatable bonds has several stable conformer structures,Įach associated with different chemical and electronic properties.Īt ambient temperatures, all the properties of that molecule are theĬombination of the properties of its conformers accessible at the Molecular conformer is a distinct conformation corresponding ![]()
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