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Deep learning for predicting synergistic drug combinations: State‐of‐the‐arts and future directions

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/jamanetworkopen.2023.46721openalex-snapshot

Performance of Large Language Models on a Neurology Board–Style Examination

inferredcites →https://doi.org/10.1007/978-1-0716-0849-4_12openalex-snapshot

Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models

inferredcites →https://doi.org/10.1007/s12539-023-00558-yopenalex-snapshot

Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network

inferredcites →https://doi.org/10.1007/s12539-023-00596-6openalex-snapshot

A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer

inferredcites →https://doi.org/10.1016/0065-2571(84)90007-4openalex-snapshot

Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors

inferredcites →https://doi.org/10.1016/j.bioactmat.2023.09.005openalex-snapshot

AI-enabled organoids: Construction, analysis, and application

inferredcites →https://doi.org/10.1016/j.csbj.2015.09.001openalex-snapshot

Searching for Drug Synergy in Complex Dose–Response Landscapes Using an Interaction Potency Model

inferredcites →https://doi.org/10.1016/j.jash.2014.09.017openalex-snapshot

Long-term safety of nebivolol and valsartan combination therapy in patients with hypertension: an open-label, single-arm, multicenter study

inferredcites →https://doi.org/10.1016/j.mtbio.2024.101014openalex-snapshot

AI energized hydrogel design, optimization and application in biomedicine

inferredcites →https://doi.org/10.1016/j.xcrm.2024.101434openalex-snapshot

A synergistic two-drug therapy specifically targets a DNA repair dysregulation that occurs in p53-deficient colorectal and pancreatic cancers

inferredcites →https://doi.org/10.1016/j.ymthe.2020.12.016openalex-snapshot

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2

inferredcites →https://doi.org/10.1016/s0140-6736(14)60614-0openalex-snapshot

Efficacy and safety of nebivolol and valsartan as fixed-dose combination in hypertension: a randomised, multicentre study

inferredcites →https://doi.org/10.1021/acs.jcim.3c00709openalex-snapshot

AttenSyn: An Attention-Based Deep Graph Neural Network for Anticancer Synergistic Drug Combination Prediction

inferredcites →https://doi.org/10.1021/acs.jcim.3c01486openalex-snapshot

CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction

inferredcites →https://doi.org/10.1021/acs.jcim.9b00927openalex-snapshot

Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach

inferredcites →https://doi.org/10.1038/s41467-021-24789-zopenalex-snapshot

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

inferredcites →https://doi.org/10.1038/s41591-023-02448-8openalex-snapshot

Large language models in medicine

inferredcites →https://doi.org/10.1038/s41597-022-01360-zopenalex-snapshot

CDCDB: A large and continuously updated drug combination database

inferredcites →https://doi.org/10.1038/s41598-023-33271-3openalex-snapshot

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy

inferredcites →https://doi.org/10.1038/s41746-024-01024-9openalex-snapshot

CancerGPT for few shot drug pair synergy prediction using large pretrained language models

inferredcites →https://doi.org/10.1038/s42256-022-00447-xopenalex-snapshot

Molecular contrastive learning of representations via graph neural networks

inferredcites →https://doi.org/10.1093/bib/bbab390openalex-snapshot

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

inferredcites →https://doi.org/10.1093/bib/bbab587openalex-snapshot

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

inferredcites →https://doi.org/10.1093/bib/bbac134openalex-snapshot

3DGT-DDI: 3D graph and text based neural network for drug–drug interaction prediction

inferredcites →https://doi.org/10.1093/bib/bbac302openalex-snapshot

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

inferredcites →https://doi.org/10.1093/bib/bbac403openalex-snapshot

Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism

inferredcites →https://doi.org/10.1093/bib/bbac503openalex-snapshot

SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction

inferredcites →https://doi.org/10.1093/bib/bbac588openalex-snapshot

CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy

inferredcites →https://doi.org/10.1093/bib/bbad167openalex-snapshot

KGANSynergy: knowledge graph attention network for drug synergy prediction

inferredcites →https://doi.org/10.1093/bib/bbad522openalex-snapshot

A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs

inferredcites →https://doi.org/10.1093/bib/bbae015openalex-snapshot

SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction

inferredcites →https://doi.org/10.1093/bioinformatics/btac579openalex-snapshot

Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction

inferredcites →https://doi.org/10.1093/bioinformatics/btad177openalex-snapshot

MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores

inferredcites →https://doi.org/10.1093/bioinformatics/btad240openalex-snapshot

SynBa: improved estimation of drug combination synergies with uncertainty quantification

inferredcites →https://doi.org/10.1093/bioinformatics/btad351openalex-snapshot

A complete graph-based approach with multi-task learning for predicting synergistic drug combinations

inferredcites →https://doi.org/10.1093/bioinformatics/btad438openalex-snapshot

DeepTraSynergy: drug combinations using multimodal deep learning with transformers

inferredcites →https://doi.org/10.1093/bioinformatics/btad607openalex-snapshot

Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor

inferredcites →https://doi.org/10.1093/bioinformatics/btv080openalex-snapshot

Large-scale exploration and analysis of drug combinations

inferredcites →https://doi.org/10.1093/bioinformatics/btx806openalex-snapshot

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

inferredcites →https://doi.org/10.1093/bioinformatics/bty593openalex-snapshot

DeepDTA: deep drug–target binding affinity prediction

inferredcites →https://doi.org/10.1093/gigascience/giac087openalex-snapshot

SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning

inferredcites →https://doi.org/10.1093/jamia/ocaa212openalex-snapshot

Anticancer drug synergy prediction in understudied tissues using transfer learning

inferredcites →https://doi.org/10.1093/jamia/ocab162openalex-snapshot

GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction

inferredcites →https://doi.org/10.1093/nar/gkaa421openalex-snapshot

SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology

inferredcites →https://doi.org/10.1093/nar/gkab438openalex-snapshot

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

inferredcites →https://doi.org/10.1093/nar/gkab913openalex-snapshot

NPCDR: natural product-based drug combination and its disease-specific molecular regulation

inferredcites →https://doi.org/10.1093/nar/gkz1007openalex-snapshot

DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy

inferredcites →https://doi.org/10.1101/2023.07.06.547759openalex-snapshot

SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design

inferredcites →https://doi.org/10.1109/bibm55620.2022.9995466openalex-snapshot

A knowledge graph embedding-based method for predicting the synergistic effects of drug combinations

inferredcites →https://doi.org/10.1109/iccvw60793.2023.00297openalex-snapshot

LLaViLo: Boosting Video Moment Retrieval via Adapter-Based Multimodal Modeling

inferredcites →https://doi.org/10.1109/jbhi.2021.3126339openalex-snapshot

Trustworthy Deep Neural Network for Inferring Anticancer Synergistic Combinations

inferredcites →https://doi.org/10.1109/tcbb.2021.3086702openalex-snapshot

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

inferredcites →https://doi.org/10.1109/tnn.2008.2005605openalex-snapshot

The Graph Neural Network Model

inferredcites →https://doi.org/10.1109/tpami.2023.3248041openalex-snapshot

Few-Shot Drug Synergy Prediction With a Prior-Guided Hypernetwork Architecture

inferredcites →https://doi.org/10.1111/bph.13895openalex-snapshot

Drug repurposing screens and synergistic drug‐combinations for infectious diseases

inferredcites →https://doi.org/10.1111/j.1744-7348.1939.tb06990.xopenalex-snapshot

THE TOXICITY OF POISONS APPLIED JOINTLY1

inferredcites →https://doi.org/10.1158/0008-5472.can-17-0489openalex-snapshot

The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity

inferredcites →https://doi.org/10.1158/0008-5472.can-18-0740openalex-snapshot

Network Propagation Predicts Drug Synergy in Cancers

inferredcites →https://doi.org/10.1158/1535-7163.mct-15-0843openalex-snapshot

An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies

inferredcites →https://doi.org/10.1158/1541-7786.mcr-21-0735openalex-snapshot

SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning

inferredcites →https://doi.org/10.1186/s12859-023-05524-5openalex-snapshot

Predicting anticancer synergistic drug combinations based on multi-task learning

inferredcites →https://doi.org/10.1186/s12859-024-05765-yopenalex-snapshot

MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction

inferredcites →https://doi.org/10.1186/s13046-020-01811-8openalex-snapshot

Vulnerability to low-dose combination of irinotecan and niraparib in ATM-mutated colorectal cancer

inferredcites →https://doi.org/10.1186/s13321-023-00690-3openalex-snapshot

DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks

inferredcites →https://doi.org/10.1186/s13321-024-00839-8openalex-snapshot

PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction

inferredcites →https://doi.org/10.12336/biomatertransl.2023.02.003openalex-snapshot

Therapeutic potential of oncolytic viruses in the era of precision oncology.

inferredcites →https://doi.org/10.12336/biomatertransl.2024.01.004openalex-snapshot

Membrane-coated nanoparticles as a biomimetic targeted delivery system for tumour therapy.

inferredcites →https://doi.org/10.1371/journal.pcbi.1006752openalex-snapshot

Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer

inferredcites →https://doi.org/10.1371/journal.pcbi.1008653openalex-snapshot

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations

inferredcites →https://doi.org/10.1371/journal.pcbi.1010951openalex-snapshot

MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders

inferredcites →https://doi.org/10.26434/chemrxiv-2022-jjm0j-v4openalex-snapshot

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

inferredcites →https://doi.org/10.3389/fchem.2019.00509openalex-snapshot

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data

inferredcites →https://doi.org/10.3390/cancers13020178openalex-snapshot

Trends in Phase II Trials for Cancer Therapies

inferredcites →https://doi.org/10.3390/ijms23179838openalex-snapshot

NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction

inferredcites →https://doi.org/10.3390/molecules28020844openalex-snapshot

DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s10916-026-02416-yopenalex-snapshot

DualKG-DC: A Drug-Centric Dual-Layer Knowledge Graph Framework for Drug Combination Prediction

inferred← citeshttps://doi.org/10.1016/b978-0-443-44430-2.00016-5openalex-snapshot

Large language models for automated drug target discovery and literature mining

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110788openalex-snapshot

Precision oncology informatics for anticancer drug combination responses: A systematic review

inferred← citeshttps://doi.org/10.1016/j.critrevonc.2025.104653openalex-snapshot

Revolutionizing prostate cancer therapy: Artificial intelligence – Based nanocarriers for precision diagnosis and treatment

inferred← citeshttps://doi.org/10.1016/s1875-5364(25)60967-8openalex-snapshot

Exploring artificial intelligence approaches for predicting synergistic effects of active compounds in traditional Chinese medicine based on molecular compatibility theory

inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00913openalex-snapshot

MSFCL: Drug Combination Risk Level Prediction Based on Multi-Source Feature Fusion and Contrastive Learning

inferred← citeshttps://doi.org/10.1021/acs.jcim.5c03141openalex-snapshot

DPSM-Synergy: A Dual-Path Feature Extraction and Synergy Matrix Enhancement Method for Anti-Cancer Drug Synergy Prediction

inferred← citeshttps://doi.org/10.1021/acsami.4c21133openalex-snapshot

Computer-Aided Discovery of Synergistic Drug–Nanoparticle Combinations for Enhanced Antimicrobial Activity

inferred← citeshttps://doi.org/10.1038/s43018-025-00917-2openalex-snapshot

Hallmarks of artificial intelligence contributions to precision oncology

inferred← citeshttps://doi.org/10.1038/s44386-025-00034-1openalex-snapshot

A review of deep learning approaches for drug synergy prediction in cancer

inferred← citeshttps://doi.org/10.1039/9781837070206-00721openalex-snapshot

Leveraging Machine Learning for Network Pharmacology

inferred← citeshttps://doi.org/10.1093/bioinformatics/btag040openalex-snapshot

One-hot news: drug synergy models shortcut molecular features

inferred← citeshttps://doi.org/10.1109/embc58623.2025.11253169openalex-snapshot

PMDSynergy: Pre-training-based multi-dimensional fusion model for cancer drug synergy prediction

inferred← citeshttps://doi.org/10.1186/s12967-025-07545-5openalex-snapshot

KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1013567openalex-snapshot

CASynergy: A causal attention model for interpretable prediction of cancer drug synergy

inferred← citeshttps://doi.org/10.3390/biology14010072openalex-snapshot

Omics-Based Interaction Analysis Reveals Interplay of Chemical Pollutant (Ozone) and Photoradiation (UVSSR) Stressors in Skin Damage

inferred← citeshttps://doi.org/10.3390/ijms27198871openalex-snapshot

Artificial Intelligence in Photodynamic Therapy: From Molecular Design of Photosensitizers to Intelligent Delivery, Adaptive Dosimetry, and Personalized Prognostication of Results

inferred← citeshttps://doi.org/10.64898/2026.06.01.26354443openalex-snapshot

Knowledge-Driven Neuro-Symbolic Reasoning for Personalized Oncology Treatment Recommendation Based on Multi-Modal Medical Knowledge Graph

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