Rep3Net: An Approach Exploiting Multimodal Representation for Molecular Bioactivity Prediction

• URL: http://arxiv.org/abs/2512.00521v1
• Date: Sat, 29 Nov 2025 15:39:48 GMT
• Title: Rep3Net: An Approach Exploiting Multimodal Representation for Molecular Bioactivity Prediction
• Authors: Sabrina Islam, Md. Atiqur Rahman, Md. Bakhtiar Hasan, Md. Hasanul Kabir,
• Abstract summary: In early stage drug discovery, bioactivity prediction of molecules against target proteins plays a crucial role.<n>We propose Rep3Net, a unified deep learning architecture that not only incorporates descriptor data but also includes spatial and relational information.<n>Our model employing multimodald features produce reliable bioactivity prediction on Poly [ADP-ribose] polymerase 1 dataset.
• Score: 0.8049701904919515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In early stage drug discovery, bioactivity prediction of molecules against target proteins plays a crucial role. Trdaitional QSAR models that utilizes molecular descriptor based data often struggles to predict bioactivity of molecules effectively due to its limitation in capturing structural and contextual information embedded within each compound. To address this challenge, we propose Rep3Net, a unified deep learning architecture that not only incorporates descriptor data but also includes spatial and relational information through graph-based represenation of compounds and contextual information through ChemBERTa generated embeddings from SMILES strings. Our model employing multimodal concatenated features produce reliable bioactivity prediction on Poly [ADP-ribose] polymerase 1 (PARP-1) dataset. PARP-1 is a crucial agent in DNA damage repair and has become a significant theraputic target in malignancies that depend on it for survival and growth. A comprehensive analysis and comparison with conventional standalone models including GCN, GAT, XGBoost, etc. demonstrates that our architecture achieves the highest predictive performance. In computational screening of compounds in drug discovery, our architecture provides a scalable framework for bioactivity prediction.

Related papers

• KGOT: Unified Knowledge Graph and Optimal Transport Pseudo-Labeling for Molecule-Protein Interaction Prediction [20.031487150263725]
  We develop an optimal transport-based approach to generate high-quality pseudo-labels for unlabeled molecule-protein pairs.<n>By treating pseudo-labeling as a mechanism for bridging disparate biological modalities, our approach enables the effective use of heterogeneous data.<n>We evaluate our framework on multiple MPI datasets including virtual screening tasks and protein retrieval tasks.
  arXiv  Detail & Related papers  (2025-12-10T06:55:36Z)
• Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling [74.25438319700929]
  We propose CHMR (Cell-aware Hierarchical Multi-modal Representations), a robust framework that models local-global dependencies between molecules and cellular responses.<n> evaluated on nine public benchmarks spanning 728 tasks, CHMR outperforms state-of-the-art baselines.<n>Results demonstrate the advantage of hierarchy-aware, multimodal learning for reliable and biologically grounded molecular representations.
  arXiv  Detail & Related papers  (2025-11-26T07:15:00Z)
• Conditional Chemical Language Models are Versatile Tools in Drug Discovery [0.0]
  We present SAFE-T, a chemical modeling framework that conditions on biological context to prioritize molecules.<n>It supports principled scoring of molecules across tasks such as virtual screening, drug-target interaction prediction, and activity cliff detection.<n>It consistently achieves performance comparable to or better than existing approaches.
  arXiv  Detail & Related papers  (2025-07-14T13:42:39Z)
• Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations [3.6099926707292793]
  Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse drug reactions with significant implications for patient safety and healthcare outcomes.<n>We propose MolecBioNet, a novel graph-based framework that integrates molecular and biomedical knowledge for robust and interpretable DDI prediction.
  arXiv  Detail & Related papers  (2025-07-12T07:43:19Z)
• PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs [88.98041407783502]
  PRING is the first benchmark that evaluates protein-protein interaction prediction from a graph-level perspective.<n> PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions.
  arXiv  Detail & Related papers  (2025-07-07T15:21:05Z)
• Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Network with Group Lasso Regularization [11.87029706744257]
  We propose a framework by implementing graph neural networks (GNNs) to predict compound-protein affinity.<n>We train GNNs with structure-aware loss functions using group lasso and sparse group lasso coloring regularizations.<n>Our approach improved property prediction by integrating common and uncommon node information with sparse group lasso.
  arXiv  Detail & Related papers  (2025-07-04T06:12:18Z)
• DISPROTBENCH: A Disorder-Aware, Task-Rich Benchmark for Evaluating Protein Structure Prediction in Realistic Biological Contexts [76.59606029593085]
  DisProtBench is a benchmark for evaluating protein structure prediction models (PSPMs) under structural disorder and complex biological conditions.<n>DisProtBench spans three key axes: data complexity, task diversity, and Interpretability.<n>Results reveal significant variability in model robustness under disorder, with low-confidence regions linked to functional prediction failures.
  arXiv  Detail & Related papers  (2025-06-18T23:58:22Z)
• KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction [60.23701115249195]
  KEPLA is a novel deep learning framework that integrates prior knowledge from Gene Ontology and ligand properties to enhance prediction performance.<n> Experiments on two benchmark datasets demonstrate that KEPLA consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-06-16T08:02:42Z)
• A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery [32.573496601865465]
  Structure-based drug discovery (SBDD) is a systematic scientific process that develops new drugs by leveraging the detailed physical structure of the target protein.<n>Recent advancements in pre-trained models for biomolecules have demonstrated remarkable success across various biochemical applications.
  arXiv  Detail & Related papers  (2025-03-06T12:04:56Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction [50.7901190642594]
  We propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner. It maintains consistent and robust semantics by smoothing relations around the target interaction.
  arXiv  Detail & Related papers  (2023-12-09T07:08:00Z)

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.