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

Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts

DrGee is presented, an essentiality-centered platform that infers drug sensitivity solely from gene expression profiles, and the built-in DeepEEAA model integrates gene expression, gene essentiality, drug-protein affinity, and drug-gene associations to quantitatively predict IC50 values.

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms

ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in NSCLC and BRCA.

Attention-enhanced Multi-omics Model for Pan-cancer Drug Response Prediction and Biomarker Discovery

Cancer drug discovery remains challenged by tumour heterogeneity and limited experimental scalability. Recently, virtual drug screening integrating machine learning algorithms has yielded numerous research results, some of which have been successfully patented and are expected to be further translated and deployed in drug discovery pipelines. Most current models for virtual drug screening fail to effectively integrate multi-omics data or capture nonlinear cross-omics interactions, restricting predictive accuracy and biomarker discovery across diverse cancers with high heterogeneity. We developed a multi-omics fusion deep learning model integrating mutation, methylation, transcriptomic, and metabolomic profiles from over 900 pan-cancer cell lines derived from the DepMap database. Our framework synergizes random forest-based feature selection to prioritize biologically relevant omics features and multi-head attention mechanisms to model nonlinear interactions between cellular multi-omics landscapes. Further biological analysis of the selected features enabled the deciphering of potential biomarkers related to drug effects. Our multi-omics fusion model attained high-performance drug response prediction across nearly 900 cancer cell lines (median Pearson r = 0.50 vs Pearson r = 0.22 for the former model for all included drugs). Validation demonstrated robust accuracy for the MEK inhibitor Trametinib (r = 0.78, MAE = 0.59) and the non-oncology agent BMOV (r = 0.73). The model identified BRAF-mutant melanoma sensitivity and PI3K/AKT bypass resistance, consistent with existing findings. Feature mining revealed TERT modulation and oxidative stress induction as BMOV's probable anticancer mechanisms, while Benzamide targeted metabolic vulnerabilities. This study introduced a deep-learning-based multi-omics fusion model to predict pan-cancer drug response. The framework achieved the expansion of the anticancer spectrum of existing anticancer drugs and explored the potential anticancer effects of non-anticancer drugs. Moreover, the integration of SHAP/MDI feature interpretation algorithms enabled mechanistic biomarker discovery. However, the prediction results that were not reported in previous research are yet to require further experimental verification. In conclusion, this work established a robust DL-driven platform for virtual drug screening and biomarker discovery, providing a computational platform that could aid virtual drug screening and biomarker discovery and facilitate the development of precision oncology.

Momentum contrast-enhanced multimodal representation learning for drug synergy prediction

Abstract Motivation Accurate prediction of synergistic drug combinations can accelerate anticancer combination discovery. Existing methods inadequately model higher order drug–drug–cell-line interactions and drug–disease associations and remain sensitive to sparse and noisy multiomics data, limiting generalization to unseen cell lines and drug combinations. Results We present Momentum Contrast (MoCo)-MultiSynergy, a multimodal framework that combines modality-specific momentum contrastive learning with heterogeneous hypergraph modeling. The hypergraph represents synergistic drug–drug–cell-line triplets and drug–disease associations, while gated residual propagation refines node representations. MoCo modules regularize encoded drug and cell-line representations using latent feature masking and Gaussian perturbation. On the O’Neil and NCI-ALMANAC datasets, MoCo-MultiSynergy achieves the highest AUROC and AUPRC across the evaluated settings, with the largest gains when generalizing to unseen cell lines and drug combinations. Availability and implementation Source code is available at https://github.com/27167199/MoCo-MultiSynergy.

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

DrugGen 2: A disease-aware language model for enhancing drug discovery

By integrating disease-specific context into molecular generation, DrugGen-2 advances AI-assisted drug discovery, offering a powerful tool for de novo design and drug repurposing that accounts for the complex interplay between diseases and molecular targets.