Research graph
References from Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow. Local targets link to admitted publications; unresolved targets remain external evidence.
A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence
10.1016/j.compbiomed.2022.106140 · 2022 · External reference
Artificial intelligence in oncology drug development and management: a precision medicine perspective
10.3389/fonc.2025.1609827 · 2025 · External reference
Unleashing the power of generative AI in drug discovery
10.1016/j.drudis.2024.103992 · 2024 · External reference
Application of perturbation gene expression profiles in drug discovery-from mechanism of action to quantitative modelling
10.3389/fsysb.2023.1126044 · 2023 · External reference
Deep learning methods for drug response prediction in cancer: predominant and emerging trends
10.3389/fmed.2023.1086097 · 2023 · External reference
Defining a cancer dependency map
10.1016/j.cell.2017.06.010 · 2017 · External reference
Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens
10.1038/s41586-019-1103-9 · 2019 · External reference
A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization
10.1016/j.ccell.2023.12.016 · 2024 · External reference
Predicting and characterizing a cancer dependency map of tumors with deep learning
10.1126/sciadv.abh1275 · 2021 · External reference
Building a translational cancer dependency map for The Cancer Genome Atlas
10.1038/s43018-024-00789-y · 2024 · External reference
Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens
10.1038/s41467-022-35378-z · 2022 · External reference
Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality
10.1016/j.cell.2014.07.027 · 2014 · External reference
Harnessing synthetic lethality to predict the response to cancer treatment
10.1038/s41467-018-04647-1 · 2018 · External reference
Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder
10.1109/jbhi.2021.3079302 · 2021 · External reference
KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers
10.1093/bioinformatics/btab271 · 2021 · External reference
KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality
10.1093/bioinformatics/btad261 · 2023 · External reference
ELISL: early-late integrated synthetic lethality prediction in cancer
10.1093/bioinformatics/btad764 · 2024 · External reference
Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction
10.1093/bib/bbae425 · 2024 · External reference
Benchmarking machine learning methods for synthetic lethality prediction in cancer
10.1038/s41467-024-52900-7 · 2024 · External reference
Pan-cancer proteomic map of 949 human cell lines
10.1016/j.ccell.2022.06.010 · 2022 · External reference
INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases
10.15252/msb.20188250 · 2019 · External reference
Comprehensive evaluation of phosphoproteomic-based kinase activity inference
10.1038/s41467-025-59779-y · 2025 · External reference
Proteome activity landscapes of tumor cell lines determine drug responses
10.1038/s41467-020-17336-9 · 2020 · External reference
Highly accurate protein structure prediction with AlphaFold
10.1038/s41586-021-03819-2 · 2021 · External reference
Accurate structure prediction of biomolecular interactions with AlphaFold 3
10.1038/s41586-024-07487-w · 2024 · External reference
Assessing the relation between protein phosphorylation, AlphaFold3 models, and conformational variability
10.1002/pro.70376 · 2026 · External reference
AI in drug discovery: clinical failures, regulatory reality, and the validation crisis behind the hype
10.3390/ph19060916 · 2026 · External reference
DiffDock: diffusion steps, twists, and turns for molecular docking
2023 · External reference
DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model
10.1038/s41467-024-45461-2 · 2024 · External reference
Deep generative molecular design and its value in modern drug discovery
10.1080/17460441.2026.2636192 · 2026 · External reference
Generative artificial intelligence based models optimization towards molecule design enhancement
10.1186/s13321-025-01059-4 · 2025 · External reference
PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences
10.1039/d3sc04185a · 2024 · External reference
Assessing the potential of deep learning for protein-ligand docking
10.1038/s42256-025-01160-1 · 2026 · External reference
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor
10.1039/d2sc05709c · 2023 · External reference
Discovery of 3-hydroxymethyl-azetidine derivatives as potent polymerase theta inhibitors
10.1016/j.bmc.2024.117662 · 2024 · External reference
Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors
10.1038/s41587-024-02526-3 · 2025 · External reference
Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain
10.1371/journal.pmed.0020073 · 2005 · External reference
Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth
10.1186/s12964-024-01954-7 · 2024 · External reference
Discovery of a bifunctional PKMYT1-targeting PROTAC empowered by AI-generation
10.1038/s41467-025-65796-8 · 2025 · External reference
The synthesizability of molecules proposed by generative models
10.1021/acs.jcim.0c00174 · 2020 · External reference
The AI-driven drug design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations
10.1007/s10822-024-00552-6 · 2024 · External reference
Scaffold-based molecular design with a graph generative model
10.1039/c9sc04503a · 2020 · External reference
The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease
10.1126/science.1132939 · 2006 · External reference
A next generation Connectivity Map: L1000 platform and the first 1,000,000 profiles
10.1016/j.cell.2017.10.049 · 2017 · External reference
A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer
10.7554/elife.71880 · 2022 · External reference
A community challenge for a pancancer drug mechanism of action inference from perturbational profile data
10.1016/j.xcrm.2021.100492 · 2022 · External reference
A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing
10.1038/s42256-020-00285-9 · 2021 · External reference
Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing
10.1016/j.patter.2022.100441 · 2022 · External reference
Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized Alzheimer's disease drug repurposing
10.1371/journal.pcbi.1010367 · 2022 · External reference
Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery
10.1038/s41467-024-49620-3 · 2024 · External reference
Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery
10.1038/s41467-024-53457-1 · 2024 · External reference
Modelling drug-induced cellular perturbation responses with a biologically informed dual-branch transformer
10.1038/s42256-025-01165-w · 2026 · External reference
Spatial perturb-seq: single-cell functional genomics within intact tissue architecture
10.1038/s41467-026-69677-6 · 2026 · External reference
Morphology and gene expression profiling provide complementary information for mapping cell state
10.1016/j.cels.2022.10.001 · 2022 · External reference
A landscape of pharmacogenomic interactions in cancer
10.1016/j.cell.2016.06.017 · 2016 · External reference
Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling
10.1038/s43018-019-0018-6 · 2020 · External reference
Predicting drug response and synergy using a deep learning model of human cancer cells
10.1016/j.ccell.2020.09.014 · 2020 · External reference
Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients
10.1038/s43018-020-00169-2 · 2021 · External reference
MOLI: multi-omics late integration with deep neural networks for drug response prediction
10.1093/bioinformatics/btz318 · 2019 · External reference
Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data
10.1186/s12859-021-04146-z · 2021 · External reference
Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
10.1038/s41467-020-19313-8 · 2020 · External reference
PharmaFormer predicts clinical drug responses through transfer learning guided by patient-derived organoid
10.1038/s41698-025-01082-6 · 2025 · External reference
Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome
10.1016/j.medj.2022.11.001 · 2023 · External reference
A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics
10.1038/s43018-024-00793-2 · 2024 · External reference
PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors
10.1038/s43018-024-00756-7 · 2024 · External reference
TranSynergy: mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations
10.1371/journal.pcbi.1008653 · 2021 · External reference
An ex vivo platform to guide drug combination treatment in relapsed/refractory lymphoma
10.1126/scitranslmed.abn7824 · 2022 · External reference
Prospective clinical validation of a combinatorial functional precision medicine platform in relapsed/refractory non-Hodgkin's lymphoma
10.1200/po-24-00780 · 2025 · External reference
Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones
10.1038/s41467-024-52980-5 · 2024 · External reference
A machine learning-based strategy predicts selective and synergistic drug combinations for relapsed acute myeloid leukemia
10.1158/0008-5472.can-24-3840 · 2025 · External reference
Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays
10.1126/sciadv.adt1851 · 2025 · External reference
Critical evaluation of drug response prediction models with DrEval
10.1038/s41467-026-72903-w · 2026 · External reference
Drug response prediction: a critical systematic review of current datasets and methods
10.1016/j.patrec.2025.10.016 · 2025 · External reference
