TL;DR
The results show that the presented MSFGCN model is very accurate and can be used reliably for ligand docking prediction and proves useful in future for rapid drug discovery and computational drug design.
A novel Dual-Attention Multimodal framework for Graphs and Sequence-based representations, so-called DAM-GS, which provides a promising solution for molecular property prediction with broad applications in drug discovery and computational molecular science.
Evaluations on eight MoleculeNet datasets show that MSMPP significantly outperforms state-of-the-art models, demonstrating its effectiveness in integrating multi-view intra-molecular features, inter-molecular features and cross-task information.
By holistically integrating atomic, motif, and global fingerprint information via hypergraph modeling, HyperMolFusion offers a more reliable computational tool to enhance the efficiency and accuracy of drug development pipelines.
INTRODUCTION Experimental identification of anticancer peptides (ACPs) is timeconsuming and costly, which limits large-scale ACP discovery and screening. To address this challenge, we developed MDFA-MLP, a novel computational framework for ACP prediction that integrates multi-scale feature learning and ensemble classification strategies. METHODS The proposed framework combines physicochemical descriptors, including amino acid composition (AAC), dipeptide composition (DPC), composition-transition-distribution (CTD), and pseudo-amino acid composition (PseAAC), with ProtBERT-derived embeddings. A Multiscale Dilated Fusion Attention (MDFA) module was designed to capture sequence patterns at different scales and enhance feature fusion. An ensemble classifier consisting of a multilayer perceptron (MLP), support vector machine (SVM), and histogram-based gradient boosting (HGB) was employed to improve prediction robustness and stability. RESULTS The proposed model was evaluated on the AntiCP 2.0 dataset under the different negative-sample settings. On Dataset A, MDFA-MLP achieved an accuracy of 93.9%, sensitivity of 92.2%, specificity of 95.8%, and an MCC of 0.89. On the more challenging Dataset B, the model achieved an accuracy of 78.2%, sensitivity of 76.5%, specificity of 82.6%, and an MCC of 0.65. Comparative experiments demonstrated that MDFA-MLP achieved competitive and balanced performance across multiple evaluation metrics. Although the improvement over existing methods was moderate in some cases, the model maintained stable predictive performance under different negative-sample settings, indicating good robustness and generalization ability. DISCUSSION The results indicate that traditional sequence descriptors and deep protein language model embeddings provide complementary biological information. The MDFA module effectively enhances feature representation by integrating multi-scale sequence characteristics, while the ensemble strategy improves model robustness and generalization under varying data distributions. CONCLUSION MDFA-MLP provides an effective and reliable framework for ACP prediction. By integrating handcrafted descriptors, protein language model representations, and ensemble learning, the proposed method can facilitate large-scale computational screening of candidate ACPs prior to experimental validation.
A novel multiview feature fusion-based graph representation model (MFF-GRM) for predicting DDI that integrates drug molecular graphs, SMILES sequences, DDI information networks, and drug biological features to learn drug features more comprehensively.
Molecular property prediction provides an important computational basis for compound screening and drug development by estimating physicochemical characteristics and biological activities from molecular structures. Although deep learning has improved molecular modeling, existing methods often describe molecules through a limited structural view or combine multiple views without sufficiently exploiting their complementary relationships. In addition, graph-based approaches commonly concentrate on local atomic connectivity, making it difficult to represent chemically meaningful structural units that may strongly influence molecular properties. This paper develops MG-CMIF, a multi-granularity cross-modal framework for molecular property prediction. The proposed model describes each molecule from symbolic, topological, and spatial perspectives and learns an integrated representation through three key designs. First, hierarchical graph modeling combines detailed atomic interactions with substructure-level chemical patterns to enrich topology-oriented features. Second, interaction across molecular views enables information relevant to property prediction to be exchanged selectively rather than merged through shallow operations. Third, alignment-oriented training objectives encourage representations derived from the same molecule to preserve compatible chemical semantics during fusion. Experiments on multiple public benchmark datasets show that MG-CMIF achieves better prediction results than competitive methods in both classification and regression settings. Further ablation analyses confirm that hierarchical structural modeling and cross-view integration both contribute to the effectiveness of the proposed framework.
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