Scoring Functions for Protein–Ligand Interactions

Sotriffer, Christoph A.

doi:10.1002/9783527645947.ch12

Cited by 14 publications

(17 citation statements)

References 139 publications

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“…The first of these reviews, which praised the ability of machine‐learning SFs for effectively exploiting very large volumes of structural and interaction data, was by Huang et al In a review of recent advances and applications of structure‐based virtual screening, Cheng et al highlighted that a pioneering machine‐learning SF strikingly outperforms 16 state‐of‐the‐art classical SFs . Furthermore, Christoph Sotriffer argued that machine‐learning SFs are becoming increasingly popular partly due to their characteristic circumvention of the sometimes problematic modeling assumptions of classical SFs . Also, when reviewing tools for analyzing protein‐drug interactions, Lahti et al highlighted that machine‐learning SFs improve the rank‐ordering of series of related molecules and that, as structural interatomic databases grow, machine‐learning SFs are expected to further improve .…”

Section: Why a Review On Machine‐learning Sfs Is Timely?mentioning

confidence: 99%

Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

Ain

Aleksandrova

Roessler

et al. 2015

WIREs Comput Mol Sci

291

234

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Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure‐based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine‐learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine‐learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert‐selected structural features can be strongly improved by a machine‐learning approach based on nonlinear regression allied with comprehensive data‐driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405–424. doi: 10.1002/wcms.1225For further resources related to this article, please visit the WIREs website.

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Section: Why a Review On Machine‐learning Sfs Is Timely?mentioning

confidence: 99%

Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

Ain

Aleksandrova

Roessler

et al. 2015

WIREs Comput Mol Sci

291

234

View full text Add to dashboard Cite

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“…This approach has been highlighted 30−33 as very promising for the improvement of scoring functions. Indeed, a growing number of studies showing the benefits of machine learning scoring functions have been presented.…”

Section: Introductionmentioning

confidence: 99%

Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

Ballester

Schreyer

Blundell

2014

J. Chem. Inf. Model.

176

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Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein–ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein–ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data.

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“…Consequently, many methods have been developed with this aim, [1,2] ranging from simple scoring and docking methods, [3,4] which can screen thousands of candidates in a short time, to strict free-energy perturbation (FEP) approaches, [5,6] which in principle should give the correct results if the sampling is exhaustive and the energy function is perfect, but at a large computational cost. The so-called end-point approaches are intermediate in computational effort and statisticalmechanics rigor.…”

Section: Introductionmentioning

confidence: 99%

Can MM/GBSA calculations be sped up by system truncation?

et al. 2017

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We have studied whether calculations of the binding free energy of small ligands to a protein by the MM/GBSA approach (molecular mechanics combined with generalized Born and surface area solvation) can be sped up by including only a restricted number of atoms close to the ligand. If the protein is truncated before the molecular dynamics (MD) simulations, quite large changes are observed for the calculated binding energies, for example, 4 kJ/mol average difference for a radius of 19 Å for the binding of nine phenol derivatives to ferritin. The results are improved if no atoms are fixed in the simulations, with average and maximum errors of 2 and 3 kJ/mol at 19 Å and 3 and 6 kJ/mol at 7 Å. Similar results are obtained for two additional proteins, p38α MAP kinase and factor Xa. On the other hand, if energies are calculated on snapshots that are truncated after the MD simulation, all residues more than 8.5 Å from the ligand can be omitted without changing the energies by more than 1 kJ/mol on average (maximum error 1.4 kJ/mol). At the molecular mechanics level, the gain in computer time for such an approach is small. However, it shows what size of system should be used if the energies instead are calculated with a more demanding method, for example, quantum-mechanics. © 2017 Wiley Periodicals, Inc.

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Scoring Functions for Protein–Ligand Interactions

Cited by 14 publications

References 139 publications

Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

Can MM/GBSA calculations be sped up by system truncation?

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