Multi-Hierarchical Fine-Grained Feature Mapping Driven by Feature Contribution for Molecular Odor Prediction

• URL: http://arxiv.org/abs/2505.00290v1
• Date: Thu, 01 May 2025 04:26:31 GMT
• Title: Multi-Hierarchical Fine-Grained Feature Mapping Driven by Feature Contribution for Molecular Odor Prediction
• Authors: Hong Xin Xie, Jian De Sun, Fan Fu Xue, Zi Fei Han, Shan Shan Feng, Qi Chen,
• Abstract summary: We propose a Feature Contribution-driven Hierarchical Multi-Feature Mapping Network (HMFNet) to address odor prediction challenges.<n> Specifically, we introduce a fine-grained, Local Multi-Hierarchy Feature Extraction module (LMFE) that performs deep feature extraction at the atomic level.<n>To enhance the extraction of discriminative atomic features, we integrate a Harmonic Modulated Feature Mapping (HMFM)<n>This module dynamically learns feature importance and frequency modulation, improving the model's capability to capture relevant patterns.
• Score: 11.166312121573277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular odor prediction is the process of using a molecule's structure to predict its smell. While accurate prediction remains challenging, AI models can suggest potential odors. Existing methods, however, often rely on basic descriptors or handcrafted fingerprints, which lack expressive power and hinder effective learning. Furthermore, these methods suffer from severe class imbalance, limiting the training effectiveness of AI models. To address these challenges, we propose a Feature Contribution-driven Hierarchical Multi-Feature Mapping Network (HMFNet). Specifically, we introduce a fine-grained, Local Multi-Hierarchy Feature Extraction module (LMFE) that performs deep feature extraction at the atomic level, capturing detailed features crucial for odor prediction. To enhance the extraction of discriminative atomic features, we integrate a Harmonic Modulated Feature Mapping (HMFM). This module dynamically learns feature importance and frequency modulation, improving the model's capability to capture relevant patterns. Additionally, a Global Multi-Hierarchy Feature Extraction module (GMFE) is designed to learn global features from the molecular graph topology, enabling the model to fully leverage global information and enhance its discriminative power for odor prediction. To further mitigate the issue of class imbalance, we propose a Chemically-Informed Loss (CIL). Experimental results demonstrate that our approach significantly improves performance across various deep learning models, highlighting its potential to advance molecular structure representation and accelerate the development of AI-driven technologies.

Related papers

• Learning Hierarchical Interaction for Accurate Molecular Property Prediction [8.488251667425887]
  We propose a Hierarchical Interaction Message Passing Mechanism, which serves as the foundation of our novel model, HimNet.<n>Our method enables interaction-aware representation learning across atomic, motif, and molecular levels via hierarchical attention-guided message passing.<n>Our method exhibits promising hierarchical interpretability, aligning well with chemical intuition on representative molecules.
  arXiv  Detail & Related papers  (2025-04-28T15:19:28Z)
• UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion [61.690978792873196]
  Existing approaches rely on either autoregressive sequence models or diffusion models.<n>We propose UniGenX, a unified framework that combines autoregressive next-token prediction with conditional diffusion models.<n>We validate the effectiveness of UniGenX on material and small molecule generation tasks.
  arXiv  Detail & Related papers  (2025-03-09T16:43:07Z)
• DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra [60.39311767532607]
  DiffMS is a formula-restricted encoder-decoder generative network. We develop a robust decoder that bridges latent embeddings and molecular structures. Experiments show DiffMS outperforms existing models on $textitde novo$ molecule generation.
  arXiv  Detail & Related papers  (2025-02-13T18:29:48Z)
• Molecular Odor Prediction with Harmonic Modulated Feature Mapping and Chemically-Informed Loss [11.654144823736143]
  We introduce a novel feature mapping method and a molecular ensemble optimization loss function.<n>Our method significantly can improve the accuracy of molecular odor prediction across various deep learning models.
  arXiv  Detail & Related papers  (2025-02-03T12:17:51Z)
• Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction [0.0]
  We introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction. DIG-Mol integrates a momentum distillation network with two interconnected networks to efficiently improve molecular characterization. We have established DIG-Mol's state-of-the-art performance through extensive experimental evaluation in a variety of molecular property prediction tasks.
  arXiv  Detail & Related papers  (2024-05-04T10:09:27Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [68.32093648671496]
  We introduce GODE, which accounts for the dual-level structure inherent in molecules.<n> Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph.<n>By pre-training two GNNs on different graph structures, GODE effectively fuses molecular structures with their corresponding knowledge graph substructures.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning [77.31492888819935]
  We propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT) MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. Experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction.
  arXiv  Detail & Related papers  (2022-12-20T19:32:30Z)
• HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention [5.735627221409312]
  We propose a well-designed hierarchical informative graph neural networks framework (termed HiGNN) for predicting molecular property. Experiments demonstrate that HiGNN achieves state-of-the-art predictive performance on many challenging drug discovery-associated benchmark datasets.
  arXiv  Detail & Related papers  (2022-08-30T05:16:15Z)
• Few-Shot Graph Learning for Molecular Property Prediction [46.60746023179724]
  We propose Meta-MGNN, a novel model for few-shot molecular property prediction. To exploit unlabeled molecular information, Meta-MGNN further incorporates molecular structure, attribute based self-supervised modules and self-attentive task weights. Extensive experiments on two public multi-property datasets demonstrate that Meta-MGNN outperforms a variety of state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-16T01:55:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.