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Topology-enhanced molecular graph representation for anti-breast cancer drug selection
Yue Gao1,2, Songling Chen1,2, Junyi Tong3
1School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.
Background:
Breast cancer is currently one of the cancers with a higher mortality rate in the world. The biological research on anti-breast cancer drugs focuses on the activity of estrogen receptors alpha (ER[Formula: see text]), the pharmacokinetic properties and the safety of the compounds, which, however, is an expensive and time-consuming process. Developments of deep learning bring potential to efficiently facilitate the candidate drug selection against breast cancer.
Methods:
In this paper, we propose an Anti-Breast Cancer Drug selection method utilizing Gated Graph Neural Networks (ABCD-GGNN) to topologically enhance the molecular representation of candidate drugs. By constructing atom-level graphs through atomic descriptors for each distinct compound, ABCD-GGNN can topologically learn both the implicit structure and substructure characteristics of a candidate drug and then integrate the representation with explicit discrete molecular descriptors to generate a molecule-level representation. As a result, the representation of ABCD-GGNN can inductively predict the ER[Formula: see text], the pharmacokinetic properties and the safety of each candidate drug. Finally, we design a ranking operator whose inputs are the predicted properties so as to statistically select the appropriate drugs against breast cancer.
Results:
Extensive experiments conducted on our collected anti-breast cancer candidate drug dataset demonstrate that our proposed method outperform all the other representative methods in the tasks of predicting ER[Formula: see text], and the pharmacokinetic properties and safety of the compounds. Extended result analysis demonstrates the efficiency and biological rationality of the operator we design to calculate the candidate drug ranking from the predicted properties.
Conclusion:
In this paper, we propose the ABCD-GGNN representation method to efficiently integrate the topological structure and substructure features of the molecules with the discrete molecular descriptors. With a ranking operator applied, the predicted properties efficiently facilitate the candidate drug selection against breast cancer.
Insights
This study introduces ABCD-GGNN, a deep learning method that enhances anti-breast cancer drug selection by analyzing molecular structures and predicting drug properties, improving efficiency and accuracy in identifying potential treatments.
Area of Science:
- Computational chemistry
- Drug discovery
- Machine learning in pharmacology
Background:
- Breast cancer remains a leading cause of cancer mortality globally.
- Traditional anti-breast cancer drug research is costly and time-consuming, focusing on estrogen receptor alpha (ERα) activity, pharmacokinetics, and safety.
- Deep learning offers a promising avenue for accelerating the identification of potential anti-breast cancer drugs.
Purpose of the Study:
- To develop an efficient deep learning method for selecting anti-breast cancer drugs.
- To enhance molecular representations of candidate drugs by integrating topological and discrete features.
- To accurately predict key drug properties including ERα activity, pharmacokinetics, and safety.
Main Methods:
- Proposed the Anti-Breast Cancer Drug selection method utilizing Gated Graph Neural Networks (ABCD-GGNN).
- Constructed atom-level graphs using atomic descriptors to capture drug topology and substructure.
- Integrated graph-based representations with discrete molecular descriptors for comprehensive molecule-level representation.
Main Results:
- ABCD-GGNN demonstrated superior performance in predicting ERα activity, pharmacokinetic properties, and safety compared to existing methods.
- Experimental results validated the efficiency and biological relevance of the proposed drug ranking operator.
- The method successfully facilitated candidate drug selection against breast cancer.
Conclusions:
- The ABCD-GGNN method effectively integrates molecular topological structure, substructure features, and discrete descriptors.
- The developed ranking operator, utilizing predicted properties, significantly aids in the selection of anti-breast cancer drugs.
- This approach offers an efficient and accurate strategy for advancing breast cancer therapeutic development.
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