Multi-Hierarchical Fine-Grained Feature Mapping Driven by Feature Contribution for Molecular Odor Prediction
Hong Xin Xie, Jian De Sun, Fan Fu Xue, Zi Fei Han, Shan Shan Feng, Qi Chen·May 01, 2025
Summary
HMFNet, a feature-driven hierarchical multi-feature mapping network, addresses odor prediction challenges. It comprises modules for deep atomic-level feature extraction, global feature learning, and dynamic feature importance learning. A Chemically-Informed Loss mitigates class imbalance. The approach significantly improves performance across deep learning models, advancing molecular structure representation and AI-driven technologies. An ablation study highlights the importance of each component, with the full model outperforming variants. Figures analyze odor descriptor frequency and co-occurrence.
Introduction
Background
Overview of odor prediction challenges
Importance of deep learning in molecular structure representation
Objective
Aim of the HMFNet approach
Contribution to AI-driven technologies in odor prediction
Method
Deep Atomic-Level Feature Extraction
Techniques for extracting detailed molecular features
Global Feature Learning
Methods for understanding the overall molecular structure
Dynamic Feature Importance Learning
Algorithms for identifying and prioritizing significant features
Chemically-Informed Loss
Description of the loss function designed to handle class imbalance
Implementation
Data Preprocessing
Steps for preparing the molecular data for analysis
Model Architecture
Detailed description of the HMFNet architecture
Training and Validation
Process for training the model and assessing its performance
Results
Performance Analysis
Comparison of HMFNet against existing models
Ablation Study
Evaluation of each component's impact on overall performance
Odor Descriptor Analysis
Figures showing frequency and co-occurrence patterns
Conclusion
Summary of Findings
Implications for Future Research
Applications in AI-driven Technologies
Basic info
papers
machine learning
artificial intelligence
Advanced features