Addressing Model Overcomplexity in Drug-Drug Interaction Prediction With Molecular Fingerprints

• URL: http://arxiv.org/abs/2503.23550v1
• Date: Sun, 30 Mar 2025 18:27:01 GMT
• Title: Addressing Model Overcomplexity in Drug-Drug Interaction Prediction With Molecular Fingerprints
• Authors: Manel Gil-Sorribes, Alexis Molina,
• Abstract summary: Accurately predicting drug-drug interactions (DDIs) is crucial for pharmaceutical research and clinical safety.<n>Recent deep learning models often suffer from high computational costs and limited generalization across datasets.<n>In this study, we investigate a simpler yet effective approach using molecular representations such as Morgan fingerprints (S), graph-based embeddings from graph convolutional networks (GCNs), and transformer-derived embeddings from MoLFormer integrated into a straightforward neural network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately predicting drug-drug interactions (DDIs) is crucial for pharmaceutical research and clinical safety. Recent deep learning models often suffer from high computational costs and limited generalization across datasets. In this study, we investigate a simpler yet effective approach using molecular representations such as Morgan fingerprints (MFPS), graph-based embeddings from graph convolutional networks (GCNs), and transformer-derived embeddings from MoLFormer integrated into a straightforward neural network. We benchmark our implementation on DrugBank DDI splits and a drug-drug affinity (DDA) dataset from the Food and Drug Administration. MFPS along with MoLFormer and GCN representations achieve competitive performance across tasks, even in the more challenging leak-proof split, highlighting the sufficiency of simple molecular representations. Moreover, we are able to identify key molecular motifs and structural patterns relevant to drug interactions via gradient-based analyses using the representations under study. Despite these results, dataset limitations such as insufficient chemical diversity, limited dataset size, and inconsistent labeling impact robust evaluation and challenge the need for more complex approaches. Our work provides a meaningful baseline and emphasizes the need for better dataset curation and progressive complexity scaling.

Related papers

• MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis [18.940529282539842]
  We construct a large-scale and precise molecular representation dataset of approximately 140,000 small molecules. Our dataset offers significant physicochemical interpretability to guide model development and design. We believe this dataset will serve as a more accurate and reliable benchmark for molecular representation learning.
  arXiv  Detail & Related papers  (2024-06-13T02:50:23Z)
• Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions [0.0]
  We introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously.
  arXiv  Detail & Related papers  (2024-04-05T17:15:48Z)
• Multiscale Topology in Interactomic Network: From Transcriptome to Antiaddiction Drug Repurposing [0.3683202928838613]
  The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment.
  arXiv  Detail & Related papers  (2023-12-03T04:01:38Z)
• MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures [2.5563339057415218]
  MolIG is a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures. It amalgamates the strengths of both molecular representation forms. It exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups.
  arXiv  Detail & Related papers  (2023-11-28T10:28:35Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity [7.023929372010717]
  We propose a novel hierarchical graph representation learning model for the drug-target binding affinity prediction, namely HGRL-DTA. In this paper, we adopt a message broadcasting mechanism to integrate the hierarchical representations learned from the global-level affinity graph and the local-level molecular graph. Besides, we design a similarity-based embedding map to solve the cold start problem of inferring representations for unseen drugs and targets.
  arXiv  Detail & Related papers  (2022-03-22T04:50:16Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions [80.12620331438052]
  deep learning has become an important tool for rapid screening of billions of molecules in silico for potential hits containing desired chemical features. Despite its importance, substantial challenges persist in training these models, such as severe class imbalance, high decision thresholds, and lack of ground truth labels in some datasets. We argue in favor of directly optimizing the receiver operating characteristic (ROC) in such cases, due to its robustness to class imbalance.
  arXiv  Detail & Related papers  (2020-06-25T08:46:37Z)
• Self-Supervised Graph Transformer on Large-Scale Molecular Data [73.3448373618865]
  We propose a novel framework, GROVER, for molecular representation learning. GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
  arXiv  Detail & Related papers  (2020-06-18T08:37:04Z)
• MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction [68.5766865583049]
  Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery. Recent years have witnessed promising progress for deep learning in DTI predictions. We propose a Molecular Interaction Transformer (TransMol) to address these limitations.
  arXiv  Detail & Related papers  (2020-04-23T18:56:04Z)

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.