Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors’ activities

Toussi, Cyrus Ahmadi; Haddadnia, Javad; Matta, Chérif F.

doi:10.1007/s11030-020-10074-6

Cited by 7 publications

(2 citation statements)

References 65 publications

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“…Matta CF explored the role of biophysical and biological properties in the formulation of QSAR models [9]. Toussi CA et al design the Ser/Thr-protein kinase inhibitors by using machine-trained elastic networks [10]. However, our group previously implemented the machine learning approaches to develop computational methods to predict the antiviral compounds against various viruses like flaviviruses, Nipah virus and coronaviruses as AVCpred [11], anti-Flavi [12] and anti-Nipah [13] and anti-corona [14], respectively.…”

Section: Introductionmentioning

confidence: 99%

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Rajput

Kumar

2021

Mol Divers

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Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure-activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC 50 values were extracted from the 'DrugRepV' database. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T 274 ) dataset. The robustness of the developed models was crossevaluated using William's plot. The highly robust computational models are integrated into the web server. The 'anti-Ebola' web server is freely available at https:// bioin fo. imtech. res. in/ manojk/ antie bola. We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus.

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Section: Introductionmentioning

confidence: 99%

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Rajput

Kumar

2021

Mol Divers

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“…Molecular property prediction refers to the effective identification of molecular properties such as lipophilicity, binding affinity, biological activity, and toxicity [2]. For fields such as drug design [3], materials science [4], and genetic engineering [5], accurate and reliable prediction of molecular properties can accelerate the development process and reduce the development cost. Therefore, molecular property prediction has significant research meaning and application value, and is a popular research at present.…”

Section: Introductionmentioning

confidence: 99%

Double-head transformer neural network for molecular property prediction

et al. 2023

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Existing molecular property prediction methods based on deep learning ignore the generalization ability of the nonlinear representation of molecular features and the reasonable assignment of weights of molecular features, making it difficult to further improve the accuracy of molecular property prediction. To solve the above problems, an end-to-end double-head transformer neural network (DHTNN) is proposed in this paper for high-precision molecular property prediction. For the data distribution characteristics of the molecular dataset, DHTNN specially designs a new activation function, beaf, which can greatly improve the generalization ability of the nonlinear representation of molecular features. A residual network is introduced in the molecular encoding part to solve the gradient explosion problem and ensure that the model can converge quickly. The transformer based on double-head attention is used to extract molecular intrinsic detail features, and the weights are reasonably assigned for predicting molecular properties with high accuracy. Our model, which was tested on the MoleculeNet [1] benchmark dataset, showed significant performance improvements over other state-of-the-art methods.

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Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms

Wang¹,

Qin²,

Yan³

2021

Mol Divers

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Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors’ activities

Cited by 7 publications

References 65 publications

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Double-head transformer neural network for molecular property prediction

Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms

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