Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction

Hamza, Hentabli; Bengherbia, Billel; Saeed, Faisal; Salim, Naomie; Nafea, Ibtehal; Abdelmoghni, Toubal; Nasser, Maged

doi:10.3390/ijms232113230

Cited by 8 publications

(5 citation statements)

References 50 publications

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“…Based on the obtained results for both algorithms, it was evident that the Neural fingerprint algorithm exhibited optimal performance compared to other Molecular fingerprints [36].…”

Section: Resultsmentioning

confidence: 94%

Neural Network-Based Adverse Drug Reaction Prediction Using Molecular Substructure Analyses

ALOK DUBEY,

KHARKAR,

PANDIT

2024

Int J App Pharm

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Objective: This study aims to enhance early detection and prediction by exploiting drug molecular substructures, overcoming challenges posed by limited authentic patient data in the medical domain. Methods: The study implemented a neural network approach to optimize molecular fingerprint algorithms and employed various machine learning algorithms for predictions. Additionally, the study identified and extracted substructures associated with severe Adverse Drug Reactions (ADRs), validating their presence within drug structures through a comparison with a random set of drug structures. Predictions were made for specific molecular structures, and results were validated using clinical evidence from the literature. Results: Optimized molecular fingerprint algorithms and diverse machine-learning models yielded promising outcomes. The Area Under Curve (AUC) value for the fingerprint dataset was obtained at approximately 65%, and integrating it with patient data significantly improved the performance by about 30%. Substructure analysis pinpointed key components linked to severe ADRs, reinforcing the predictive prowess of the model. Predictions for specific molecular structures were corroborated using clinical evidence from the literature, fortifying the credibility of the proposed approach. Conclusion: In conclusion, this research effectively tackles challenges in the early detection and prediction of ADRs by leveraging machine learning algorithms, focusing on drug molecular substructures. The optimized model, incorporating both fingerprint and patient datasets, demonstrated significant improvements in predictive performance. Identifying and validating substructures linked to severe ADRs contribute to the model's reliability. The study's findings are vital for advancing drug safety and laying the groundwork for further strides in predictive modeling within the medical domain.

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“…Based on the obtained results for both algorithms, it was evident that the Neural fingerprint algorithm exhibited optimal performance compared to other Molecular fingerprints [36].…”

Section: Resultsmentioning

confidence: 94%

Neural Network-Based Adverse Drug Reaction Prediction Using Molecular Substructure Analyses

ALOK DUBEY,

KHARKAR,

PANDIT

2024

Int J App Pharm

View full text Add to dashboard Cite

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“…Convolutional neural network (CNN) is widely used in molecular image learning to identify molecular features in the field of drug activity prediction. For example, Hentabli et al [39] developed a molecular matrix format adapted from two-dimensional fingerprint descriptors to predict the biological activity of compounds based on deep learning convolutional neural network. The area under the curve (AUC) of the CNN activity prediction method was the highest.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Integrated Learning Activity Prediction Model of BHO-AdaBoosting Anti-Breast Cancer ERα Inhibitor Based on Improved Random Forest

Du,

Xu,

Huang

et al. 2023

Preprint

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Breast cancer is the most common malignancy in women worldwide. The pathogenesis of this disease is closely related to the estrogen receptor alpha subtype (ERα). Therefore, it is of great importance to develop effective inhibitors of ERα activity for the treatment of breast cancer. In this paper, we propose a novel ensemble machine learning model for quantitative structure-activity relationship of anti-breast cancer drugs, which can effectively predict drug activity in small samples with multiple characteristic variables. To avoid the problem of over-fitting caused by low-correlation independent variables, the scoring mechanism of random forest was improved by incorporating three relevance indicators, including the maximum mutual information number, Pearson correlation coefficient and distance correlation coefficient, and 20 optimal molecular descriptors were selected. The Bayesian hyperparameter optimization method was used to optimize the parameters of multiple linear regression (MLR), support vector regression (SVR), and extreme gradient boosting (XGBoost), respectively. The AdaBoost strong learner was constructed by combining the weak learner with the weighted linear addition method. The results show that the proposed ensemble learning model has the best prediction performance compared to the three basic learner models and the CNN-LSTM combination prediction model. The root mean square error was reduced by 7.60%-26.51%. The mean relative error was reduced by 6.46%-30.92%. Goodness of fit increased by 9.57%-36.94%. Finally, the biological activities of 50 candidate compounds for ERα inhibitors were predicted, and it was found that 4-[2-benzyl-1-[4-(2-pyrrolidin-1-ylethoxy)phenyl]but-1-enyl]phenol had an excellent biological activity value pIC50, which had the potential to be an ERα inhibitor. The model proposed in this paper has good prediction accuracy, which can provide an effective reference for the discovery and development of anti-breast cancer drugs.

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“…Improving the prediction precision of protein-ligand binding affinity is crucial in drug discovery. Hentabli et al (2022) focused on developing a deep learning approach to predict the biological activity of compounds, introducing a novel technique using a convolutional neural network (CNN) model. The model was evaluated using standard datasets with homogenous and heterogenous activity categories (MDL Drug Data Report and Sutherland).…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

New Methods for Predicting Drug Molecule Activity Using Deep Learning

White

2024

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With the rapid development of deep learning technology, its application in predicting drug molecule activity is becoming increasingly widespread. This study reviews the latest progress and applications of deep learning in the field of drug discovery, especially in predicting drug molecule activity. It focuses on discussing several major deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Graph Neural Networks (GNN), and how they help improve the accuracy and efficiency of drug activity prediction. Additionally, the importance of interdisciplinary collaboration in promoting the application of deep learning in drug discovery is explored, and directions for future research are proposed, including improving model interpretability, optimizing data quality, and expanding the application of deep learning technology. This study aims to provide researchers and drug development experts with a comprehensive and in-depth perspective on the potential and challenges of deep learning in predicting drug molecule activity, while also offering insights and references for research and development in related fields.

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Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction

Cited by 8 publications

References 50 publications

Neural Network-Based Adverse Drug Reaction Prediction Using Molecular Substructure Analyses

Neural Network-Based Adverse Drug Reaction Prediction Using Molecular Substructure Analyses

Integrated Learning Activity Prediction Model of BHO-AdaBoosting Anti-Breast Cancer ERα Inhibitor Based on Improved Random Forest

New Methods for Predicting Drug Molecule Activity Using Deep Learning

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