DeepD_DrugC: Deep and distributed workflow to predict drug- candidates

Sid, Karima; Zertal, Soumia; Mezioud, Chaker

doi:10.1109/pais56586.2022.9946898

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Among ML methods, NNs or deep learning NNs built on the Keras/TensorFlow platform have been used so far mainly in binary classification tasks in drug design [91]. Here, we showed that NNs can also be used in drug design as efficient multi-class classifiers when trained on the datasets with discrete compound activity values [54].…”

Section: Discussionmentioning

confidence: 86%

Keras/TensorFlow in Drug Design for Immunity Disorders

Dragan,

Joshi,

Atzei

et al. 2023

Preprint

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Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel com-pound scaffolds acting on three chemokine receptors: CCR2, CCR3 and one CXC receptor CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier, but as an efficient multi-class classifier that can discard not only inactive compounds but also low or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 were among the top predicted compounds and thus benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors available at: http://db-gpcr-chem.uw.edu.pl. The NN model trained on the cannabinoid receptor datasets re-trieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.

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Section: Discussionmentioning

confidence: 86%

Keras/TensorFlow in Drug Design for Immunity Disorders

Dragan,

Joshi,

Atzei

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Among ML methods, NNs or deep learning NNs built on the Keras/TensorFlow platform have been used so far mainly in binary classification tasks in drug design [95].…”

Section: Discussionmentioning

confidence: 99%

Keras/TensorFlow in Drug Design for Immunity Disorders

Dragan,

Joshi,

Atzei

et al. 2023

IJMS

View full text Add to dashboard Cite

Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3, and one CXC receptor, CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier but as an efficient multi-class classifier that can discard not only inactive compounds but also low- or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 compounds were among the top predicted compounds; thus, the benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.

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DeepD_DrugC: Deep and distributed workflow to predict drug- candidates

Cited by 2 publications

References 21 publications

Keras/TensorFlow in Drug Design for Immunity Disorders

Keras/TensorFlow in Drug Design for Immunity Disorders

Keras/TensorFlow in Drug Design for Immunity Disorders

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