Momentum contrast-enhanced multimodal representation learning for drug synergy prediction

SynerMolNet: Molecular Representation and Attention Fusion for Cancer Drug Synergy Prediction

Multimodal contrastive learning for integrating molecular representations and cellular phenotypes in drug-target interaction prediction

A two-stage contrastive learning framework integrating drug structures, protein sequences, and Cell Painting morphological profiles into a unified embedding space, which reveals pathway-specific morphological signatures associated with drug targets, providing biologically interpretable insights into drug mechanisms.

Molecular Fragment-Based Graph Isomorphism Networks for Interpretable Prediction of Synergistic Drug Combinations

Results indicate that FragSyn, through the synergistic design of fragment-level representation and cellular context awareness, provides an effective and interpretable new approach to synergistic drug combination prediction.

NELLY enables patient-centric drug prioritization through interpretable drug-conditioned gene weighting

A translational framework combining patient-centric benchmarking with a pan-cancer pharmacogenomic atlas of patient-derived organoids and a deep learning model integrating transcriptomic and chemical information to predict drug response and prioritize therapies is introduced, supporting NELLY as a promising framework for translationally relevant and interpretable drug response prediction in precision oncology.

Knowledge-driven multimodal mutual learning for cell line-targeted anticancer peptide prediction.

MOTIVATION As unique drugs positioned between small and macro molecules, anticancer peptides (ACPs) hold great potential in oncotherapy owing to their high selectivity and low toxicity. Nowadays, computational ACP prediction has emerged as a cost-effective alternative to bioassay screening, but most methods are limited to identifying bioactivity and fail to resolve tumor cell-specific targeting, primarily because of the sparse annotated data. RESULTS To fill this gap, we integrate a hybrid dataset compiled from five well-established peptide databases and propose TargetPC, a deep learning method tailored for cell line-targeted ACP prediction. TargetPC encodes multimodal representations of ACPs and cell lines via pretrained protein and omics models, and combines them via hierarchical intra- and inter-modal fusion for targeting prediction. This combination is further augmented by a mutual learning paradigm that distills domain knowledge from both ACP and cell line, enabling improved generalization under sparse supervision. Experimental results on the hybrid dataset demonstrate the effectiveness of TargetPC, which outperforms the state-of-the-art baselines in terms of prediction accuracy, and maintains strong generalization to unseen ACPs and cell lines. When extended to out-of-distribution samples, TargetPC has successfully screened dozens of novel ACPs targeted to breast cancer cells and uncovered biological motifs underlying its predictions. As a result, our TargetPC is expected to serve as a versatile tool for lead ACP discovery at a lower burden. AVAILABILITY The source code and data are available at GitHub (https://github.com/liuxuan666/TargetPC).

MKASynergy: an adaptive method for drug synergy prediction via a mixture-of-experts kernel mechanism

MKASynergy, an adaptive drug synergy prediction method based on a mixture-of-experts kernel mechanism, achieves competitive predictive performance and visualization analysis confirms the model’s effectiveness in feature decoupling and helps interpret latent drug synergistic mechanisms.