A community computational challenge to predict the activity of pairs of compounds

Cited by (320)

Items 101–200 of 320

1. Biomolecular Network-Based Synergistic Drug Combination Discovery
   Xiangyi Li, Guangrong Qin, Qingmin Yang, Lanming Chen, Lu Xie · 2016 · BioMed Research International
2. Looking beyond the cancer cell for effective drug combinations
   Jonathan R. Dry, Mi Yang, Julio Sáez-Rodríguez · 2016 · Genome Medicine
3. Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer
   Sung‐Young Shin, Anna‐Katharina Müller, Nandini Verma, Sima Lev, Lan K. Nguyen · 2018 · PLoS Computational Biology
4. KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
   Isidro Cortés‐Ciriano, Andreas Bender · 2019 · Apollo (University of Cambridge)
5. A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer
   Delora Baptista, Pedro Gabriel Ferreira, Miguel Rocha · 2023 · PLoS Computational Biology
6. Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes
   Yiyi Liu, Hongyu Zhao · 2016 · Bioinformatics
7. Synergy Maps: exploring compound combinations using network-based visualization
   Richard Lewis, Rajarshi Guha, Tamás Korcsmáros, Andreas Bender · 2015 · Journal of Cheminformatics
8. Prediction of synergistic drug combinations
   Zohar Weinstein, Andreas Bender, Murat Cokol · 2017 · Current Opinion in Systems Biology
9. Providing data science support for systems pharmacology and its implications to drug discovery
   Thomas D. Hart, Lei Xie · 2015 · Expert Opinion on Drug Discovery
10. Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method
    Mohan Timilsina, Meera Tandan, Mathieu d’Aquin, Haixuan Yang · 2019 · Scientific Reports
11. Computational frameworks transform antagonism to synergy in optimizing combination therapies
    Jinghong Chen, Anqi Lin, Aimin Jiang, Chang Qi, Zaoqu Liu, Quan Cheng, Shuofeng Yuan, Peng Luo · 2025 · npj Digital Medicine
12. AI-driven discovery of synergistic drug combinations against pancreatic cancer
    Mohsen Pourmousa, Sankalp Jain, Elena S. Barnaeva, Wengong Jin, Joshua E. Hochuli, Zina Itkin, Travis Maxfield, Cleber Camilo Melo-Filho, Andrew Thieme, Kelli M. Wilson et al. · 2025 · Nature Communications
13. To Combine or Not Combine: Drug Interactions and Tools for Their Analysis. Reflections from the EORTC-PAMM Course on Preclinical and Early-phase Clinical Pharmacology
    Btissame El Hassouni, Giulia Mantini, Giovanna Li Petri, Mjriam Capula, Lenka N.C. Boyd, Hannah N. W. Weinstein, Andrea Vallés‐Martí, Mathilde C.M. Kouwenhoven, Elisa Giovannetti, Bart A. Westerman et al. · 2019 · Anticancer Research
14. Resistance to antifungal therapies
    Rajendra Prasad, Atanu Banerjee, Abdul Haseeb Shah · 2017 · Essays in Biochemistry
15. Drug Response Prediction by Globally Capturing Drug and Cell Line Information in a Heterogeneous Network
    Duc‐Hau Le, Van-Huy Pham · 2018 · Journal of Molecular Biology
16. Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects
    Pieter Dewulf, Michiel Stock, Bernard De Baets C · 2021 · Pharmaceuticals
17. Machine learning to design antimicrobial combination therapies: Promises and pitfalls
    Jennifer M. Cantrell, Carolina H. Chung, Sriram Chandrasekaran · 2022 · Drug Discovery Today
18. Grouped-seq for integrated phenotypic and transcriptomic screening of patient-derived tumor organoids
    Yushuai Wu, Kaiyi Li, Yaqian Li, Tao Sun, Chang Liu, Chunhui Dong, Tian Zhao, Decong Tang, Xiaojie Chen, Xiaofang Chen et al. · 2021 · Nucleic Acids Research
19. Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible
    Mark Sinzger-D’Angelo, Jakob Vanhoefer, Carolin Loos, Jan Hasenauer · 2019 · Scientific Reports
20. Synthesis of Silver Nano Particles Using Myricetin and the In-Vitro Assessment of Anti-Colorectal Cancer Activity: In-Silico Integration
    Syed Tauqeer Anwer, Mohammad Mobashir, Omer I. Fantoukh, Bushra Khan, Khalid Imtiyaz, Irshad Hussain Naqvi, M. Moshahid A. Rizvi · 2022 · International Journal of Molecular Sciences
21. CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient Health State
    Xiang Li, Shunpan Liang, Yu Lei, Chen Li, Yulei Hou, Dashun Zheng, Tengfei Ma · 2024 · ACM International Conference on Information and Knowledge Management (CIKM)
22. Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations
    Jing Ping Tang · 2017 · Methods in molecular biology
23. PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients
    Youngchul Kim, Daewon Kim, Biwei Cao, Rodrigo Carvajal, Minjung Kim · 2020 · BMC Bioinformatics
24. A landscape for drug-target interactions based on network analysis
    Edgardo Galán-Vásquez, Ernesto Pérez‐Rueda · 2021 · PLoS ONE
25. Modeling drug combination effects via latent tensor reconstruction
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26. Dual Inhibition of Autophagy and PI3K/AKT/MTOR Pathway as a Therapeutic Strategy in Head and Neck Squamous Cell Carcinoma
    Monique Bernard, Guillaume B. Cardin, Maxime Cahuzac, Tareck Ayad, Éric Bissada, Louis Guertin, Houda Bahig, Phuc Félix Nguyen‐Tan, Édith Filion, Olivier Ballivy et al. · 2020 · Cancers
27. Synthetic essentiality: Targeting tumor suppressor deficiencies in cancer
    Di Zhao, Ronald Anthony DePinho · 2017 · BioEssays
28. Challenge-Enabled Machine Learning to Drug-Response Prediction
    Ziyan Wang, Hongyang Li, Christopher F Carpenter, Yuanfang Guan · 2020 · The AAPS Journal
29. Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies
    Shengnan She, Hengwei Chen, Wei Ji, Mengqiu Sun, Jiaxi Cheng, Mengjie Rui, Chunlai Feng · 2022 · Frontiers in Pharmacology
30. DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning
    Zhongming Wang, Jiahui Dong, Lianlian Wu, Chong Dai, Jing Wang, Yuqi Wen, Yixin Zhang, Xiaoxi Yang, Song He, Xiaochen Bo · 2023 · Molecules
31. Fibronectin is a stress responsive gene regulated by HSF1 in response to geldanamycin
    Karim Colin Hassan Dhanani, William John Samson, Adrienne Lesley Edkins · 2017 · Scientific Reports
32. Searching Synergistic Dose Combinations for Anticancer Drugs
    Zuojing Yin, Zeliang Deng, Wenyan Zhao, Zhiwei Cao · 2018 · Frontiers in Pharmacology
33. A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer
    Fatemeh S. Seyednasrollah, Devin C. Koestler, Tao Wang, Stephen Piccolo, Roberto Vega, Russell Greiner, Christiane Fuchs, Eyal Gofer, Luke Kumar, Russell D. Wolfinger et al. · 2017 · JCO Clinical Cancer Informatics
34. Mitigating temozolomide resistance in glioblastoma via DNA damage-repair inhibition
    Inmaculada C. Sorribes, Samuel K. Handelman, Harsh Vardhan Jain · 2020 · Journal of The Royal Society Interface
35. Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing
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36. MeSIN: Multilevel selective and interactive network for medication recommendation
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37. Knowledge graph embeddings in the biomedical domain: are they useful? A look at link prediction, rule learning, and downstream polypharmacy tasks
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38. DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived xenograft models
    Quanxue Li, Wentao Dai, Jixiang Liu, Yixue Li, Yuanyuan Li · 2019 · Journal of Translational Medicine
39. Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network
    Namgil Lee, Hojin Yoo, Heejung Yang · 2021 · Biomolecules
40. Model-based computational precision medicine to develop combination therapies for autoimmune diseases
    Emiko Desvaux, Audrey Aussy, Sandra Hubert, Florence Keime-Guibert, Alexia Blesius, Perrine Soret, Mickaël Guedj, Jacques‐Olivier Pers, Laurence Laigle, Philippe E. Moingeon · 2021 · Expert Review of Clinical Immunology
41. Validation of a non-oncogene encoded vulnerability to exportin 1 inhibition in pediatric renal tumors
    Diego F. Coutinho, Prabhjot Singh Mundi, Lianna Jean Marks, Chelsey Burke, Michael Vincent Ortiz, Daniel Diolaiti, Lauren Bird, Kelly Vallance, Glorymar Ibáñez, Daoqi You et al. · 2022 · Med
42. Unlocking the therapeutic potential of drug combinations through synergy prediction using graph transformer networks
    Waleed Alam, Hilal Tayara, Kil To Chong · 2024 · Computers in Biology and Medicine
43. Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies
    Bogdan Mazoure, Robert Nadon, Vladimir Makarenkov · 2017 · Scientific Reports
44. High-Throughput Screening for Drug Combinations
    Paul Shinn, Lu Chen, Marc Ferrer, Zina Itkin, Carleen A. Klumpp-Thomas, Crystal G. McKnight, Sam Michael, Tim Mierzwa, Craig J. Thomas, Kelli M. Wilson et al. · 2019 · Methods in molecular biology
45. Application of perturbation gene expression profiles in drug discovery—From mechanism of action to quantitative modelling
    Bence Szalai, Dániel V. Veres · 2023 · Frontiers in Systems Biology
46. A microfluidic Braille valve platform for on-demand production, combinatorial screening and sorting of chemically distinct droplets
    Ramesh Utharala, Anna Luise Grab, Vida Vafaizadeh, Nicolas Peschke, Martine Ballinger, Dénes Türei, Nadine Tuechler, Wenwei Ma, Olga Ivanova, Alejandro Gil Ortiz et al. · 2022 · Nature Protocols
47. Molecular Changes During Acute Myeloid Leukemia (AML) Evolution and Identification of Novel Treatment Strategies Through Molecular Stratification
    Ella Karjalainen, Gretchen A. Repasky · 2016 · Progress in molecular biology and translational science
48. Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes
    Yuan Quan, Meng‐Yuan Liu, Ye-Mao Liu, Lida Zhu, Yu‐Shan Wu, Zhi-Hui Luo, Xiuzhen Zhang, Shizhong Xu, Qingyong Yang, Hongyu Zhang · 2018 · Molecules
49. Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization
    Pingjian Ding, Cong Shen, Zihan Lai, Cheng Liang, Guanghui Li, Jiawei Luo · 2019 · Journal of Chemical Information and Modeling
50. MSFF-MA-DDI: Multi-Source Feature Fusion with Multiple Attention blocks for predicting Drug–Drug Interaction events
    Qi Jin, Jiang Xie, Dingkai Huang, Chang Zhao, Hongjian He · 2023 · Computational Biology and Chemistry
51. A novel computational solution to the health risk assessment of air pollution via joint toxicity prediction: A case study on selected PAH binary mixtures in particulate matters
    Xian Liu, Huazhou Zhang, Wenxiao Pan, Qiao Xue, Jianjie Fu, Guorui Liu, Minghui Zheng, Aiqian Zhang · 2018 · Ecotoxicology and Environmental Safety
52. Polypharmacy and precision medicine
    Fujita, Kenji, Masnoon, Nashwa, Mach, John, O’Donnell, Lisa Kouladjian, Hilmer, Sarah N. · 2023 · Cambridge Prisms: Precision Medicine
53. A multi-herb-combined remedy to overcome hyper-inflammatory response by reprogramming transcription factor profile and shaping monocyte subsets
    Gejing De, Apeng Chen, Qinghe Zhao, Ran Xie, Chaoxi Wang, Meng Li, Haiyu Zhao, Xinru Gu, Lauren H. McCarl, Fangbo Zhang et al. · 2021 · Pharmacological Research
54. A network paradigm predicts drug synergistic effects using downstream protein–protein interactions
    Jennifer L. Wilson, Ethan Steinberg, Rebecca Racz, Russ Biagio Altman, Nigam Haresh Shah, Kevin Grimes · 2022 · CPT Pharmacometrics & Systems Pharmacology
55. Detection of polypharmacy side effects by integrating multiple data sources and convolutional neural networks
    Amir Lakizadeh, Mahdi Babaei · 2022 · Molecular Diversity
56. DREAMTools: a Python package for scoring collaborative challenges
    Thomas Cokelaer, Mukesh Bansal, Christopher Bare, Erhan Bilal, Brian M. Bot, Elias Chaibub Neto, Federica Eduati, Alberto de la Fuente, Mehmet Gönen, Steven M. Hill et al. · 2016 · F1000Research
57. Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism
    Jan Wildenhain, Michaela Spitzer, Sonam Dolma, Nick Jarvik, Rachel White, Marcia M. Roy, Emma Griffiths, David S. Bellows, Gerard D. Wright, Mike Tyers · 2016 · Scientific Data
58. On the robustness of generalization of drug–drug interaction models
    Rogia Kpanou, Mazid Abiodoun Osseni, Prudencio Tossou, François Laviolette, Jacques Corbeil · 2021 · BMC Bioinformatics
59. An Integrative Pharmacogenomic Approach Identifies Two-drug Combination Therapies for Personalized Cancer Medicine
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60. Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference
    Tingting Zheng, Yueqiong Ni, Jun Li, Bkc Chow, Gianni Panagiotou · 2017 · Frontiers in Physiology
61. DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration
    Minsik Oh, Sungjoon Park, Sangseon Lee, Dohoon Lee, Sangsoo Lim, Dabin Jeong, Kyuri Jo, Inuk Jung, Sun Kim · 2020 · Frontiers in Genetics
62. H-RACS: a handy tool to rank anti-cancer synergistic drugs
    Xinmiao Yan, Yi‐Yan Yang, Zikun Chen, Zuojing Yin, Zeliang Deng, Tianyi Qiu, Kailin Tang, Zhiwei Cao · 2020 · Aging
63. Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics
    Shiqi Li, Fuhui Zhang, Xiuchan Xiao, Yanzhi Guo, Zhining Wen, Menglong Li, Xuemei Pu · 2021 · Frontiers in Pharmacology
64. Predicting drug synergy for precision medicine using network biology and machine learning
    Ali Cüvitoğlu, Joseph Zhou, Sui Huang, Zerrin Işık · 2019 · Journal of Bioinformatics and Computational Biology
65. DREAMTools: a Python package for scoring collaborative challenges
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66. MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction
    Baofang Hu, Zhenmei Yu, Mingke Li · 2024 · Molecules
67. A human metabolic map of pharmacological perturbations reveals drug modes of action
    Laurentz Schuhknecht, Karin Ortmayr, Jürgen Jänes, Martina Bläsi, Eleni Panoussis, Sebastian Bors, Terézia Dorčaková, Tobias Fuhrer, Pedro Beltrão, Mattia Zampieri · 2025 · Nature Biotechnology
68. Drug-Drug Interaction Prediction: a Purely SMILES Based Approach
    Bri Bumgardner, Farhan Tanvir, Khaled Mohammed Saifuddin, Esra Akbaş · 2021 · IEEE International Conference on Big Data (BigData)
69. HDACIs and TKIs combinations and their liposomal delivery for cancer treatment
    Cindy Schelker, Patrycja M. Nowak-Sliwinska, Gerrit Borchard · 2023 · Journal of Controlled Release
70. MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations
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71. Modeling polypharmacy effects with heterogeneous signed graph convolutional networks
    Taoran Liu, Jiancong Cui, Hui Zhuang, Hong Wang · 2021 · Applied Intelligence
72. Predicting Drug Synergism by Means of Non-Negative Matrix Tri-Factorization
    Pietro Pinoli, Gaia Ceddia, Stefano Ceri, Marco Masseroli · 2021 · IEEE Transactions on Computational Biology and Bioinformatics
73. Triple combination of BET plus PI3K and NF-κB inhibitors exhibit synergistic activity in adult T-cell leukemia/lymphoma
    Anusara Daenthanasanmak, Richard N. Bamford, Makoto Yoshioka, Shyh‐Ming Yang, Philip Homan, Baktiar O. Karim, Bonita R. Bryant, Michael N. Petrus, Craig J. Thomas, Patrick L. Green et al. · 2022 · Blood Advances
74. A simple gene set-based method accurately predicts the synergy of drug pairs
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75. An integrated framework for identification of effective and synergistic anti-cancer drug combinations
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76. Patient derived tumoroids of high grade neuroendocrine neoplasms for more personalized therapies
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77. From cell lines to cancer patients: personalized drug synergy prediction
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78. A synthetic lethal dependency on casein kinase 2 in response to replication-perturbing therapeutics in RB1-deficient cancer cells
    Daria R. Bulanova, Yevhen A. Akimov, Wojciech Senkowski, Jaana Oikkonen, Laura Gall-Mas, Sanna Timonen, Manar Elmadani, Johanna Hynninen, Sampsa Hautaniemi, Tero A. Aittokallio et al. · 2024 · Science Advances
79. Computational Drug Repurposing for Neurodegenerative Diseases
    Kyriaki Savva, Margarita Zachariou, Anastasis Oulas, George Minadakis, Kleitos Sokratous, Nikolas Dietis, George M. Spyrou · 2019 · Elsevier eBooks
80. Enhancing Drug Synergy Combination: Integrating Graph Transformers and BiLSTM for Accurate Drug Synergy Prediction
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81. Single-cell multi-omics reveals that FABP1 + renal cell carcinoma drive tumor angiogenesis through the PLG-PLAT axis under fatty acid reprogramming
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82. MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations
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83. DDI-SSL: Drug–Drug Interaction Prediction Based on Substructure Signature Learning
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84. ImageDDI: Image-enhanced molecular motif sequence representation for drug–drug interaction prediction
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85. Predicting Cancer Drug Response: Advancing the DREAM
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86. Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian Hierarchical Approach
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87. Identification of Key Genes as Potential Drug Targets for Gastric Cancer
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88. Prediction of Cancer Treatment Using Advancements in Machine Learning
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89. Anticancer Evaluation of Methoxy Poly(Ethylene Glycol)-b-Poly(Caprolactone) Polymeric Micelles Encapsulating Fenbendazole and Rapamycin in Ovarian Cancer
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90. PARP-1-dependent RND1 transcription induced by topoisomerase I cleavage complexes confers cellular resistance to camptothecin
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91. Pairwise combinations of chemical compounds that delay yeast chronological aging through different signaling pathways display synergistic effects on the extent of aging delay
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92. ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets
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93. Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool
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94. DRGATAN: Directed relation graph attention aware network for asymmetric drug-drug interaction prediction
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95. Predicting anti-cancer drug combination responses with a temporal cell state network model
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96. Predicting Polypharmacy Side Effects Based on an Enhanced Domain Knowledge Graph
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98. An In Silico Method for Predicting Drug Synergy Based on Multitask Learning
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99. A novel network based linear model for prioritization of synergistic drug combinations
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100. Predicting Drug-drug Interactions Using Heterogeneous Graph Attention Networks
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