ArticleFrontiers in bioengineering and biotechnology2022
MLP-Based Regression Prediction Model For Compound Bioactivity.
Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- Improved leaf area index estimation in Jujube trees by fusing spatial-temporal features from UAV RGB time-series with a deep learning framework.Frontiers in plant science · 2026Article
- Unraveling potent Glycyrrhiza glabra flavonoids as AKT1 inhibitors using network pharmacology and machine learning-assisted QSAR.Molecular diversity · 2025Article
- Predicting the quality of digital mammography from the perspective of detectability of microcalcifications: A Radiomics approach.Medical physics · 2025Article
- Chemotaxonomy, an Efficient Tool for Medicinal Plant Identification: Current Trends and Limitations.Plants (Basel, Switzerland) · 2025Review
- Identifying the key influencing factors of psychological birth trauma in primiparous women with interpretable machine learning.International journal of nursing sciences · 2025Article
- Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution.Scientific reports · 2025Article
- Development of a biomimetic nanoparticle platform for apigenin therapy in triple-negative breast cancer.Frontiers in oncology · 2025Article
- Prediction of postoperative vault after implantable collamer lens implantation with deep learning.International journal of ophthalmology · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Deep learning-guided discovery of selective JAK2-JH2 allosteric inhibitors: integration of MLP predictive modeling, BREED-based library design, and computational validation.Frontiers in chemistry · 2025Article
- Identifying High-Quality Leads among Screened Anticancerous Compounds Using SMILES Representations.ACS omega · 2024Article
- Machine learning methods for adult OSAHS risk prediction.BMC health services research · 2024Article
- The role of dsRNA A-to-I editing catalyzed by ADAR family enzymes in the pathogeneses.RNA biology · 2024Review
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The development of breast cancer is closely linked to the estrogen receptor ERα, which is also considered to be an important target for the treatment of breast cancer. Therefore, compounds that can antagonize ERα activity may be drug candidates for the treatment of breast cancer. In drug development, to save manpower and resources, potential active compounds are often screened by establishing compound activity prediction model. For the 1974 compounds collected, the top 20 molecular descriptors that significantly affected the biological activity were screened using LASSO regression models combined with 10-fold cross-validation method. Further, a regression prediction model based on the MLP fully connected neural network was constructed to predict the bioactivity values of 50 new compounds. To measure the validity of the model, the model loss term was specified as the mean squared error (MSE). The results showed that the MLP-based regression prediction model had a loss value of 0.0146 on the validation set. This model is therefore well trained and the prediction strategy used is valid. The methods developed by this paper may provide a reference for the development of anti-breast cancer drugs.
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