Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets

Abdelbaky, Ibrahim; Tayara, Hilal; Chong, Kil To

doi:10.1038/s41598-020-80758-4

Cited by 19 publications

(12 citation statements)

References 44 publications

(70 reference statements)

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“…In detail, the class of descriptors ‘AlogP’, ‘Atom Count’, ‘Autocorrelation’, ‘Aromatic atoms count’, ‘Barysz matrix’, ‘BCUT’, ‘Burden matrix’, and ‘Atom type electrotopological state’ were present within each kinase-specific feature set. Moreover, this result was comparable with the outcome of Abdelbaky et al work, where the authors found that these descriptors reported a good ability to differentiate between the active and inactive classes within the kinase family 20 . This would suggest that these descriptors could well characterize the inhibitors of the examined kinases.…”

Section: Resultssupporting

confidence: 89%

“…Indeed, in 2020 Miljković et al 18 applied different machine learning approaches to generate models on the basis of compounds with binding modes confirmed by X-ray crystallography for predicting different classes of kinase inhibitors (including types I, I1/2, and II as well as allosteric inhibitors). Similarly, in 2021 Abdelbaky et al 20 described the application of predictive models to discriminate between four binding modes: three allosteric inhibitor modes (I, II, I1/2) and one non-allosteric mode. The high accuracy rate of both works demonstrated that the new machine learning models have considerable potential for practical applications.…”

Section: Introductionmentioning

confidence: 99%

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KUALA: a machine learning-driven framework for kinase inhibitors repositioning

Simone

Sardina

Gulotta

et al. 2022

Sci Rep

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The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases.

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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

KUALA: a machine learning-driven framework for kinase inhibitors repositioning

Simone

Sardina

Gulotta

et al. 2022

Sci Rep

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“…The decoding network consists of LSTM layers. LSTM as a special RNN structure has proven successful and is widely used for modeling long-range dependencies in various previous studies [11,12,17,23]. The contribution of LSTM is recurrent connections, memory cell C t , and the self-parameterized and controlling gates, which essentially controls the flow of the state information.…”

Section: Convlstm-lstm Architecture For Energy Load Forecastingmentioning

confidence: 99%

“…However, these models have the limitation of dealing with the nonlinear nature of energy load data. To tackle this problem of nonlinearity, deep learning and machine learning have become very popular in recent times, inspired by stateof-the-art achievements in image classification [7], natural language processing [8], protein synthesis [9,10], drug discovery [11], and robotics [12]. Deep neural network architectures have the ability to learn complex data representations of the datasets, which alleviates the need for manual feature engineering and model design, considered to be a time-consuming and tidy job.…”

Section: Introductionmentioning

confidence: 99%

Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System

2021

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An efficient energy management system is integrated with the power grid to collect information about the energy consumption and provide the appropriate control to optimize the supply–demand pattern. Therefore, there is a need for intelligent decisions for the generation and distribution of energy, which is only possible by making the correct future predictions. In the energy market, future knowledge of the energy consumption pattern helps the end-user to decide when to buy or sell the energy to reduce the energy cost and decrease the peak consumption. The Internet of things (IoT) and energy data analytic techniques have provided the convenience to collect the data from the end devices on a large scale and to manipulate all the recorded data. Forecasting an electric load is fairly challenging due to the high uncertainty and dynamic nature involved due to spatiotemporal pattern consumption. Existing conventional forecasting models lack the ability to deal with the spatio-temporally varying data. To overcome the above-mentioned challenges, this work proposes an encoder–decoder model based on convolutional long short-term memory networks (ConvLSTM) for energy load forecasting. The proposed architecture uses encode consisting of multiple ConvLSTM layers to extract the salient features in the data and to learn the sequential dependency and then passes the output to the decoder, having LSTM layers to make forecasting. The forecasting results produced by the proposed approach are favorably comparable to the existing state-of-the-art and better than the conventional methods with the least error rate. Quantitative analyses show that a mean absolute percentage error (MAPE) of 6.966% for household energy consumption and 16.81% for city-wide energy consumption is obtained for the proposed forecasting model in comparison with existing encoder–decoder-based deep learning models for two real-world datasets.

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“…ML models have shown the ability to distinguish between multitarget and single-target kinase inhibitors [ 40 ] as well as robust performance in predicting different chemical classes of kinase inhibitors [ 41 ]. By representing kinase inhibitors as a large number of molecular descriptors, feature-based ML models can provide an accurate classification of kinase probes according to their binding modes [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration

Krishnan

Kassab

Agajanian

et al. 2022

IJMS

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In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemical latent space. The proposed strategy combines autoencoder-based embedding of small molecules with a cluster-based perturbation approach for efficient navigation of the latent space and a feature-based kinase inhibition likelihood classifier that guides optimization of the molecular properties and targeted molecular design. In the proposed generative approach, molecules sharing similar structures tend to cluster in the latent space, and interpolating between two molecules in the latent space enables smooth changes in the molecular structures and properties. The results demonstrated that the proposed strategy can efficiently explore the latent space of small molecules and kinase inhibitors along interpretable directions to guide the generation of novel family-specific kinase molecules that display a significant scaffold diversity and optimal biochemical properties. Through assessment of the latent-based and chemical feature-based binary and multiclass classifiers, we developed a robust probabilistic evaluator of kinase inhibition likelihood that is specifically tailored to guide the molecular design of novel SRC kinase molecules. The generated molecules originating from LCK and ABL1 kinase inhibitors yielded ~40% of novel and valid SRC kinase compounds with high kinase inhibition likelihood probability values (p > 0.75) and high similarity (Tanimoto coefficient > 0.6) to the known SRC inhibitors. By combining the molecular perturbation design with the kinase inhibition likelihood analysis and similarity assessments, we showed that the proposed molecular design strategy can produce novel valid molecules and transform known inhibitors of different kinase families into potential chemical probes of the SRC kinase with excellent physicochemical profiles and high similarity to the known SRC kinase drugs. The results of our study suggest that task-specific manipulation of a biased latent space may be an important direction for more effective task-oriented and target-specific autonomous chemical design models.

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Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets

Cited by 19 publications

References 44 publications

KUALA: a machine learning-driven framework for kinase inhibitors repositioning

KUALA: a machine learning-driven framework for kinase inhibitors repositioning

Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System

Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration

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