Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein–ligand interactions based on a study on caspase 3 inhibitors

Erdas, Ozlem; Andac, Cenk A.; Gurkan‐Alp, A. Selen; Alpaslan, Ferda Nur; Büyükbingöl, Erdem

doi:10.3109/14756366.2014.976566

Cited by 3 publications

(23 citation statements)

References 29 publications

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“…Support vector regression and PLSR methods were applied to the resulting X‐, Y‐, Z‐, and XYZ‐feature vectors for predicting pIC 50 values of CHK1‐ligand interactions. In contrast with the previous versions of CIFAP examining that the linear regression models fit better to the features obtained from the compressed images of protein‐ligand complexes, the SVR with linear kernel was preferred for the prediction phase rather than the SVR with RBF kernel that was explained by Erdas et al A grid search using leave‐one‐out cross‐validation was applied for optimizing 2 main SVR parameters, which are C, the adjustment value between error handling and generalization, and ε, ε‐tube's radius. The best values were chosen among the ones that achieve the minimum RMSE and the maximum R 2 values for X‐, Y‐, Z‐ and XYZ‐feature vectors, separately.…”

Section: Resultsmentioning

confidence: 99%

“…Electrostatic properties are crucial parameters for defining interactions between proteins and ligands. In our studies, we attempted to use electrostatic grid maps of protein‐ligand binding sites for predicting the binding affinity of the complexes. Although satisfying RMSE values were obtained as the results of binding affinity prediction, the usage of every electrostatic charge corresponding to a single point on a 3D grid map was expensive for feature selection and prediction operations, which took days to calculate.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, we experimented the applicability of the CIFAP method predicting the binding affinities of a novel series of ligands derived from 2‐aminothiazole‐4‐carboxamide interacting with CHK1 protein. The CIFAP method has 2 phases, which include the modeling of 3D structure of the binding site into 2D images and predicting the binding affinities using the resulting predictive model.…”

Section: Resultsmentioning

confidence: 99%

“…Prediction phase of CIFAP tries to find the relationship between SFFS features acquired from compressed 2D images and the binding affinity defined as observed pIC 50 values. In this study, 2 prediction algorithms were implemented by MATLAB (MATLAB version 7.12) to evaluate CIFAP on CHK1 inhibitors, SVR and PLSR, which were previously described by Erdas et al as a prediction technique used in the evolution of CIFAP. Instead of SVR with radial basis function (RBF) kernel, which was used in the experiments of previous CIFAP versions, SVR with linear kernel was preferred in the prediction phase.…”

Section: Methodsmentioning

confidence: 99%

“…In our previous studies, we proposed compressed images for binding affinity prediction (CIFAP), a novel data representation scheme for protein‐ligand interactions to be used for prediction of binding affinity. The CIFAP has 2 phases, namely, data modeling and prediction phases.…”

Section: Introductionmentioning

confidence: 99%

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An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Konyar

Erdas

Alpaslan

et al. 2017

J of Molecular Recognition

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Investigation of protein-ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein-ligand model, identifying 2D electrostatic potential images by separating the binding site of protein-ligand complexes and using the images for predicting the computational affinity information represented by pIC values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein-ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2-aminothiazole-4-carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1-ligand complexes with low-error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Konyar

Erdas

Alpaslan

et al. 2017

J of Molecular Recognition

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Artificial Intelligence in Chemistry: Current Trends and Future Directions

Baum¹,

Yu²,

Ayala³

et al. 2021

J. Chem. Inf. Model.

129

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The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.

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Three-Dimensional Analysis of Binding Sites for Predicting Binding Affinities in Drug Design

Cicek

Atac

Gurkan‐Alp

et al. 2019

J. Chem. Inf. Model.

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Understanding the interaction between drug molecules and proteins is one of the main challenges in drug design. Several tools have been developed recently to decrease the complexity of the process. Artificial intelligence and machine learning methods offer promising results in predicting the binding affinities. It becomes possible to do accurate predictions by using the known protein–ligand interactions. In this study, the electrostatic potential values extracted from 3-dimensional grid cubes of the drug–protein binding sites are used for predicting binding affinities of related complexes. A new algorithm with a dynamic feature selection method was implemented, which is derived from Compressed Images For Affinity Prediction (CIFAP) study, to predict binding affinities of Checkpoint Kinase 1 and Caspase 3 inhibitors.

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Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein–ligand interactions based on a study on caspase 3 inhibitors

Cited by 3 publications

References 29 publications

An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Artificial Intelligence in Chemistry: Current Trends and Future Directions

Three-Dimensional Analysis of Binding Sites for Predicting Binding Affinities in Drug Design

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