Conclusion
We propose an explainable fragment-based molecular property attribution method for analyzing the relevance between the biochemical property and molecular fragments. Moreover, statistical results and mechanism verification are adopted to demonstrate the reliability of discovered relevance between molecular property and fragments. Experiments on forty-two biochemical property tasks show that about 90% of the attribution fragments strongly relate to the corresponding property task, and random-selected attribution results from six classical side effect property tasks satisfy the biochemical mechanism excellently. The discovered relationship between molecular property and fragments can be applied to various tasks, such as exploring the relation of different molecular properties and targeted property molecular synthesis with specific fragments. Based on the attribution fragment sequence for different property tasks, we build the property relation map of all the forty-two properties. The transfer learning experiments are adopted to verify the benefits of the property relation map for assisting rapid and accurate transfer learning performance. In summary, as a computer-assisted molecular discovery method, our fragment-based attribution method can provide pharmacologists with sufficiently precise guidance, accelerate the process of analyzing the properties of drug molecules, and promote the efficiency of clinical trials. In future work, we will focus on using more information to represent the characteristics of molecules, such as adding dihedral angles in the three-dimensional conformation and realizing more natural molecular tree decomposition methods to achieve more precise positioning.
Methods
Acknowledgements
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Conflict of interest
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