Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions

Moman, Edelmiro; Grishina, Maria; Потемкин, В. А.

doi:10.1007/s10822-019-00248-2

Cited by 7 publications

(3 citation statements)

References 56 publications

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“…Calculation of the thermodynamic and surface characteristics of SnO 2 thin films at all temperatures was carried out using MERA software with periodic boundary conditions along a and b axes of SnO 2 unit cell like it was described in [18,19] and applied in studying organic, inorganic and combined systems in [18 -36]. The MOPS algorithm has also shown previous successful use in modeling oxyhydrate gel formation [18 -36], crystal structures of triosmium clusters [20,21], complexation of organic molecules during chemical reactions [22,24,28,33,35], and crystal structures and interaction energies of gas hydrates [18,19]. The measured energies, thermodynamic characteristics (such as enthalpies, entropies, and Gibbs free energies), modeled structures of the complexes, crystals, and clusters, predicted yields, rates, and regio-and stereo-specificity of the reactions, and predicted yields, rates, and regio-and stereo-specificity of the reactions were all in good agreement with experimental results previously stated in the publications mentioned above.…”

Section: Methodsmentioning

confidence: 99%

Computational and experimental studies on SnO2 thin films at various temperatures

Gurushankar,

Grishina,

Gohulkumar

et al. 2023

Computer Optics

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Tin oxide (SnO2) thin films was prepared by dip-coating technique at various bath temperatures (313, 333, 353 and 373 K) and annealed at 673 K in this study. And the obtained results were studied and correlated with the computational method. Scanning electron microscopy (SEM) investigation demonstrated that the prepared samples are spherical with agglomeration. The elemental analysis (EDAX) confirms the presence of Sn and O. Further, the SnO2 thin films microstructures are simulated, their thermodynamic and surface properties have been calculated. Micro-Raman spectra were recorded for the prepared samples. Micro-Raman results exhibit the first-order Raman mode E1gsub> (475 cm−1) indicating that the grown SnO2 belongs to the rutile structure. In addition, the envelope method used for studying optical characteristics of the thin films from the transmittance spectra. The semiconducting nature of the films has been noticed from linear I-V characteristics. Furthermore, the electrical conductivity studies suggest that the highest conductivity samples acquire the lowest activation energy and their values are also in the semiconducting range.

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Section: Methodsmentioning

confidence: 99%

Computational and experimental studies on SnO2 thin films at various temperatures

Gurushankar,

Grishina,

Gohulkumar

et al. 2023

Computer Optics

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“…However, the B factor is mainly available from crystallography structures and it is indeed hard work to predict the B factor for other structures. Other studies that adopt specific energy features are similar, 59–65 and here we will not show more details.…”

Section: Handcrafted Featuresmentioning

confidence: 95%

Featurization strategies for protein–ligand interactions and their applications in scoring function development

Xiong

Shen

Yang

et al. 2021

WIREs Comput Mol Sci

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The predictive performance of classical scoring functions (SFs) seems to have reached a plateau. Currently, SFs relying on sophisticated machine learning techniques have shown great potential in binding affinity prediction and virtual screening. As one of the most indispensable components in the workflow of training a machine learning scoring function (MLSF), the featurization or representation process enables us to catch certain physical processes that are important for protein–ligand interactions and to obtain machine‐readable descriptors. Currently, according to how they are derived, the descriptors used in MLSFs for both continuous and binary binding affinity estimates can be grouped into two broad categories: handcrafted features and automated‐extraction features. Moreover, the automated‐extraction features emerge as a new featurization trend along with the application of deep learning algorithms. Here, we make a thorough summary of the advances in the featurization strategies for protein–ligand interactions in the context of MLSFs, with emphasis on the recently rising automated‐extraction features. We also discuss the similarity between protein–ligand interaction representations and small‐molecule representations, and the challenges confronted by the scientific community in characterizing protein–ligand interactions. We expect that this review could inspire the development of novel featurization approaches and boosted MLSFs. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Interactions

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“…The performances of ML-based SFs are highly dependent on the input features. Generally speaking, input features can be specific energy features, − protein–ligand atom pairwise counts or potentials, − interaction fingerprints, − mathematical features, − grid-based features, − graph-based features, ,− etc. Unlike other machine learning algorithms, many deep learning-based SFs can automatically extract features and use them for training. , Currently, extensive efforts still rely on the use of traditional ML to improve the scoring power of SFs.…”

Section: Introductionmentioning

confidence: 99%

Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules

Dong

Zhang

et al. 2022

J. Chem. Inf. Model.

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Water molecules at the ligand–protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to improve the prediction performance of existing ML-based SFs, we estimated the water distribution with a HydraMap (HM) method and then incorporated the features extracted from protein-bound waters obtained in this way into three ML-based SFs: RF-Score, ECIF, and PLEC. It was found that a combination of HM-based features can consistently improve the performance of all three SFs, including their scoring, ranking, and docking power. HydraMap-based features show consistently good performance with both crystal structures and docked structures, demonstrating their robustness for SFs. Overall, HM-based features, which are a statistical representation of hydration sites at protein–ligand interfaces, are expected to improve the prediction performance for diverse SFs.

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Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions

Cited by 7 publications

References 56 publications

Computational and experimental studies on SnO2 thin films at various temperatures

Computational and experimental studies on SnO2 thin films at various temperatures

Featurization strategies for protein–ligand interactions and their applications in scoring function development

Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules

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