PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies

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The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

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A Landscape of Pharmacogenomic Interactions in Cancer

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DrugBank 4.0: shedding new light on drug metabolism

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Pharmacogenomics Knowledge for Personalized Medicine

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Biomarkers in Cancer Staging, Prognosis and Treatment Selection

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The ChEMBL bioactivity database: an update

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Artificial Intelligence in Cancer Research and Precision Medicine

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Machine learning approaches to drug response prediction: challenges and recent progress

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Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response

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Optimization of cell viability assays to improve replicability and reproducibility of cancer drug sensitivity screens

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Artificial intelligence, machine learning, and drug repurposing in cancer

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CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics

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

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Cell Model Passports-a hub for clinical, genetic and functional datasets of preclinical cancer models

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AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships

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Dr.VAE: improving drug response prediction via modeling of drug perturbation effects

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Machine learning in the prediction of cancer therapy

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Exploration of databases and methods supporting drug repurposing: a comprehensive survey

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Machine learning for perturbational single-cell omics

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Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

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Truncated FGFR2 is a clinically actionable oncogene in multiple cancers

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A cross-study analysis of drug response prediction in cancer cell lines

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Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care

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Learning with multiple pairwise kernels for drug bioactivity prediction

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In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

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Identification of a five-gene signature of the RGS gene family with prognostic value in ovarian cancer

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An overview of machine learning methods for monotherapy drug response prediction

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The role and application of bioinformatics techniques and tools in drug discovery

Zhang, S., Liu, K., Liu, Y., Hu, X., & Gu, X. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1547131 · Cited by 82

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data

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Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models

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DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features

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RNAactDrug: a comprehensive database of RNAs associated with drug sensitivity from multi-omics data

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Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

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Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones

Ianevski, A., Nader, K. M., Driva, K., Senkowski, W., Bulanova, D. R., Moyano‐Galceran, L., Ruokoranta, T. M., Kuusanmäki, H. A., Ikonen, N., Sergeev, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52980-5 · Cited by 67

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

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Targeted inhibition of KDM6 histone demethylases eradicates tumor-initiating cells via enhancer reprogramming in colorectal cancer

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DrugR+: A comprehensive relational database for drug repurposing, combination therapy, and replacement therapy

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A combination strategy targeting enhancer plasticity exerts synergistic lethality against BETi-resistant leukemia cells

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Biological and therapeutic implications of a unique subtype of NPM1 mutated AML

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A Comparative Study of Cluster Detection Algorithms in Protein–Protein Interaction for Drug Target Discovery and Drug Repurposing

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Genome-wide DNA methylation patterns reveal clinically relevant predictive and prognostic subtypes in human osteosarcoma

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Interactive visual analysis of drug–target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing

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Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction

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Combined Minimum-Run Resolution IV and Central Composite Design for Optimized Removal of the Tetracycline Drug Over Metal–Organic Framework-Templated Porous Carbon

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Synergy between Non-Thermal Plasma with Radiation Therapy and Olaparib in a Panel of Breast Cancer Cell Lines

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More than fishing for a cure: The promises and pitfalls of high throughput cancer cell line screens

Ling, A. L., Gruener, R. F., Fessler, J. L., & Huang, R. S. (2018) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2018.06.014 · Cited by 35

Bayesian multi-source regression and monocyte-associated gene expression predict BCL-2 inhibitor resistance in acute myeloid leukemia

White, B. S., Khan, S. A., Mason, M. J., Ammad-ud-din, M., Potdar, S., Malani, D., Kuusanmäki, H. A., Druker, B. J., Heckman, C. A., Kallioniemi, O., et al. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00209-9 · Cited by 33

The Integrative Analysis of Thrombospondin Family Genes in Pan-Cancer Reveals that THBS2 Facilitates Gastrointestinal Cancer Metastasis

Zhang, C., Hu, C. W., Su, K., Wang, K., Du, X., Xing, B., & Liu, X. (2021) · Journal of Oncology · doi:10.1155/2021/4405491 · Cited by 35

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer’s disease drug repurposing

Wu, Y., Liu, Q., Qiu, Y., & Xie, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010367 · Cited by 34

The pan-cancer landscape of netrin family reveals potential oncogenic biomarkers

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The mevalonate pathway is an actionable vulnerability of t(4;14)-positive multiple myeloma

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Assessment of Genetic Drift in Large Pharmacogenomic Studies

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Trends in Deep Learning for Property-driven Drug Design

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Pharmacogenomics and big genomic data: from lab to clinic and back again

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PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis

Feizi, N., Nair, S. K., Smirnov, P., Beri, G., Eeles, C. B., Esfahani, P. N., Nakano, M., Tkachuk, D., Mammoliti, A., Gorobets, E., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1084 · Cited by 31

Combined Cellular and Biochemical Profiling to Identify Predictive Drug Response Biomarkers for Kinase Inhibitors Approved for Clinical Use between 2013 and 2017

Uitdehaag, J. C. M., Kooijman, J. J., de Roos, J. A. D. M., Prinsen, M. B. W., Dylus, J., Willemsen‐Seegers, N., Kawase, Y., Sawa, M., de Man, J., van Gerwen, S. J. C., et al. (2018) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-18-0877 · Cited by 30

Integrative analysis the characterization of peroxiredoxins in pan-cancer

Gao, L., Meng, J., Yue, C., Wu, X., Su, Q., Wu, H., Zhang, Z., Yu, Q., Gao, S., Fan, S., et al. (2021) · Cancer Cell International · doi:10.1186/s12935-021-02064-x · Cited by 28

Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data

Potdar, S., Ianevski, F., Ianevski, A., Tanoli, Z., Wennerberg, K., Seashore-Ludlow, B. A., Kallioniemi, O., Östling, P., Aittokallio, T. A., & Saarela, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad390 · Cited by 23

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Sun, J., Xu, M., Ru, J., James-Bott, A., Xiong, D., Wang, X., & Cribbs, A. P. (2023) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2023.115500 · Cited by 27

Anticarin-β shows a promising anti-osteosarcoma effect by specifically inhibiting CCT4 to impair proteostasis

Wang, G., Zhang, M., Meng, P., Long, C., Luo, X., Yang, X., Wang, Y., Zhang, Z., Mwangi, J., Kamau, P. M., et al. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2021.12.024 · Cited by 25

Cancer driver drug interaction explorer

Hartung, M., Anastasi, E., Mamdouh, Z. M., Nogales, C., Schmidt, H. H. H. W., Baumbach, J., Zolotareva, O. I., & List, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac384 · Cited by 24

Encircling the regions of the pharmacogenomic landscape that determine drug response

Fernández‐Torras, A., Duran‐Frigola, M., & Aloy, P. (2019) · Genome Medicine · doi:10.1186/s13073-019-0626-x · Cited by 24

Advances and critical aspects in cancer treatment development using digital twins

Bouriga, R., Bailleux, C., Gal, J., Chamorey, E., Mograbi, B., Hannoun‐Lévi, J., & Milano, G. A. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf237 · Cited by 20

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Gondal, M. N., & Chaudhary, S. U. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.712505 · Cited by 19

The Aurora kinase/β-catenin axis contributes to dexamethasone resistance in leukemia

Shah, K., Ahmed, M., & Kazi, J. U. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00148-5 · Cited by 21

Mining Drug-Target Associations in Cancer: Analysis of Gene Expression and Drug Activity Correlations

Arroyo, M., Berral-González, A., Bueno-Fortes, S., Alonso‐López, D., & Rivas, J. D. L. (2020) · Biomolecules · doi:10.3390/biom10050667 · Cited by 18

A Personalized Therapeutics Approach Using an In Silico Drosophila Patient Model Reveals Optimal Chemo- and Targeted Therapy Combinations for Colorectal Cancer

Gondal, M. N., Butt, R. N., Shah, O. S., Sultan, M. U., Mustafa, G., Nasir, Z., Hussain, R., Khawar, H., Qazi, R., Tariq, M., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.692592 · Cited by 17

The Systematic Analyses of RING Finger Gene Signature for Predicting the Prognosis of Patients with Hepatocellular Carcinoma

Zhang, C., Yang, Y., Wang, K., Chen, M., Lu, M., Hu, C. W., Du, X., Xing, B., & Liu, X. (2022) · Journal of Oncology · doi:10.1155/2022/2466006 · Cited by 17

Evidence of Omics, Immune Infiltration, and Pharmacogenomic for SENP1 in the Pan-Cancer Cohort

Taghvaei, S., Sabouni, F., & Minuchehr, Z. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.700454 · Cited by 17

Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data

Caroli, J., Dori, M., & Bicciato, S. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00185 · Cited by 16

MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma

Lietz, C. B., Garbutt, C. C., Barry, W. T., Deshpande, V., Chen, Y., Lozano‐Calderón, S. A., Wang, Y. E., Lawney, B. P., Ebb, D. H., Cote, G. M., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-61236-3 · Cited by 16

Multi‐modal meta‐analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Jaiswal, A., Gautam, P., Pietilä, E. A., Timonen, S., Nordström, N., Akimov, Y. A., Sipari, N., Tanoli, Z., Fleischer, T., Lehti, K., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209526 · Cited by 15

Use of Germline Genetic Variability for Prediction of Chemoresistance and Prognosis of Breast Cancer Patients

Hlaváč, V., Kováčová, M., Elsnerová, K., Brynychová, V., Koževnikovová, R., Rauš, K., Kopečková, K., Měšťáková, S., Vrána, D., Gatěk, J., et al. (2018) · Cancers · doi:10.3390/cancers10120511 · Cited by 16

MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines

Tran, T.-O., Nguyen, T.-H., Nguyen, T. T., & Le, N. Q. K. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02182-3 · Cited by 15

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

Bintener, T., Pacheco, M. P., & Sauter, T. (2020) · Biochemical Society Transactions · doi:10.1042/bst20190867 · Cited by 17

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

Piyawajanusorn, C., Nguyen, L. C., Ghislat, G., & Ballester, P. J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab312 · Cited by 18

Drug Repurposing: Insights into Current Advances and Future Applications

Bhatia, T., & Sharma, S. (2023) · Current Medicinal Chemistry · doi:10.2174/0109298673266470231023110841 · Cited by 16

Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines

Koras, K., Kizling, E., Juraeva, D., Staub, E., & Szczurek, E. (2021) · Scientific Reports · doi:10.1038/s41598-021-94564-z · Cited by 16

Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity

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Inference of drug off-target effects on cellular signaling using interactome-based deep learning

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