Identification of thrombopoiesis inducer based on a hybrid deep neural network model

Mo, Qi; Zhang, Ting; Wu, Jianming; Wang, Long; Luo, Jiesi

doi:10.1016/j.thromres.2023.04.011

Cited by 3 publications

(5 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model identified methylophiopogonanone A as a potential therapeutic agent for RIT, demonstrating its ability to enhance MK differentiation and platelet production [ 17 ]. Additionally, our team screened wedelolactone, a compound promoting MK differentiation, using hybrid recurrent neural network (RNN) and DNN models [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks

Yi,

Luo,

Liao

et al. 2024

Biomolecules

Self Cite

View full text Add to dashboard Cite

(1) Background: Radiation-induced thrombocytopenia (RIT) often occurs in cancer patients undergoing radiation therapy, which can result in morbidity and even death. However, a notable deficiency exists in the availability of specific drugs designed for the treatment of RIT. (2) Methods: In our pursuit of new drugs for RIT treatment, we employed three deep learning (DL) algorithms: convolutional neural network (CNN), deep neural network (DNN), and a hybrid neural network that combines the computational characteristics of the two. These algorithms construct computational models that can screen compounds for drug activity by utilizing the distinct physicochemical properties of the molecules. The best model underwent testing using a set of 10 drugs endorsed by the US Food and Drug Administration (FDA) specifically for the treatment of thrombocytopenia. (3) Results: The Hybrid CNN+DNN (HCD) model demonstrated the most effective predictive performance on the test dataset, achieving an accuracy of 98.3% and a precision of 97.0%. Both metrics surpassed the performance of the other models, and the model predicted that seven FDA drugs would exhibit activity. Isochlorogenic acid A, identified through screening the Chinese Pharmacopoeia Natural Product Library, was subsequently subjected to experimental verification. The results indicated a substantial enhancement in the differentiation and maturation of megakaryocytes (MKs), along with a notable increase in platelet production. (4) Conclusions: This underscores the potential therapeutic efficacy of isochlorogenic acid A in addressing RIT.

show abstract

Section: Introductionmentioning

confidence: 99%

An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks

Yi,

Luo,

Liao

et al. 2024

Biomolecules

Self Cite

View full text Add to dashboard Cite

show abstract

“…This chemical collection consisted of both FDA-approved drugs and natural products curated from various laboratory screening sublibraries at our institute. 18,20 K562 cells (Bethesda, MD, USA) were cultured in complete RPMI 1640 medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin, incubated in a 5% CO 2 -humidified atmosphere at 37 °C. Next, cells in logarithmic growth phase were seeded in 24-well plates at a density of 2 × 10 4 cells/well.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…We previously developed a hybrid deep learning (HDL) for predicting megakaryopoiesis inducers, incorporating features learned by a recurrent neural network (RNN) and a deep neural network (DNN) to capture both sequence and structural features of molecules. 20 The input of the HDL model is the one-hot encoding of SMILES strings combined with 2048-bit Morgan circular fingerprints.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…This hypothesis gains further support from another active compound, wedelolactone, where 1,2-diphenol emerges as the most crucial substructure. 20 Additionally, the substructure carboxyl derivative also makes important contributions to the prediction of wedelolactone, thus suggesting its potential as another active functional group. Finally, we analyzed avatrombopag (Doptelet), an orally administered smallmolecule thrombopoietin receptor (c-Mpl) agonist that elevates the platelet count without inducing platelet activation.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…In the same year, Wang et al further improved the performance of classification model using the multigrained cascade forest (gcForest) algorithm. 18 20 Despite significant research progress, several challenges remain unresolved in accurately predicting thrombopoiesis inducers using computational methods. First, while these methods share the common goal of prediction, they differ in algorithms and incorporation of prior knowledge.…”

Section: ■ Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Active Molecules against Thrombocytopenia through Machine Learning

Yang,

Gan,

Lin

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Thrombocytopenia, which is associated with thrombopoietin (TPO) deficiency, presents very limited treatment options and can lead to life-threatening complications. Discovering new therapeutic agents against thrombocytopenia has proven to be a challenging task using traditional screening approaches. Fortunately, machine learning (ML) techniques offer a rapid avenue for exploring chemical space, thereby increasing the likelihood of uncovering new drug candidates. In this study, we focused on computational modeling for drug-induced megakaryocyte differentiation and platelet production using ML methods, aiming to gain insights into the structural characteristics of hematopoietic activity. We developed 112 different classifiers by combining eight ML algorithms with 14 molecule features. The top-performing model achieved good results on both 5-fold cross-validation (with an accuracy of 81.6% and MCC value of 0.589) and external validation (with an accuracy of 83.1% and MCC value of 0.642). Additionally, by leveraging the Shapley additive explanations method, the best model provided quantitative assessments of molecular properties and structures that significantly contributed to the predictions. Furthermore, we employed an ensemble strategy to integrate predictions from multiple models and performed in silico predictions for new molecules with potential activity against thrombocytopenia, sourced from traditional Chinese medicine and the Drug Repurposing Hub. The findings of this study could offer valuable insights into the structural characteristics and computational prediction of thrombopoiesis inducers.

show abstract

AI-Driven Decision-Making Applications in Pharmaceutical Sciences

Yingngam,

Navabhatra,

Sillapapibool

2024

Advances in Media, Entertainment, and the Arts

View full text Add to dashboard Cite

This chapter explores AI's influence on pharmaceutical sciences, highlighting its enhancement of traditional design methodologies. It explores AI's transformational role in key sectors, including drug discovery, virtual screening, and drug formulation development. AI's ability to efficiently identify potential drug candidates from large chemical libraries and its use of optimization algorithms in the selection of suitable excipients and dosage forms are discussed. The chapter also emphasizes AI's significance in improving pharmaceutical manufacturing processes through parameter refinement, quality outcome prediction, and real-time anomaly detection. The integration of traditional design methods with AI ensures robust, reliable, AI-driven processes that are compliant with regulations. In conclusion, the chapter highlights the potential of AI in pharmaceutical sciences and the importance of its integration with traditional design methods. This approach empowers scientists to innovate, speed up drug development, and improve patient outcomes.

show abstract

Identification of thrombopoiesis inducer based on a hybrid deep neural network model

Cited by 3 publications

References 46 publications

An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks

An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks

Identification of Active Molecules against Thrombocytopenia through Machine Learning

AI-Driven Decision-Making Applications in Pharmaceutical Sciences

Contact Info

Product

Resources

About