ArticleInternational journal of molecular sciences2019
A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.Abdominal radiology (New York) · 2026Pooled it
- Current status and trends of machine learning applied in clinical research of gastric cancer from 2004 to 2023: global bibliometric and visual analysis.Frontiers in oncology · 2025Pooled it
- ComplexMatrixComb: predicting anticancer drug combination dosages for target growth inhibition via complex-number matrix factorization.Journal of cheminformatics · 2026Article
- EPIC: A Machine-Learning Framework for Product-Dependent Behavior in β‑Glucosidases.ACS omega · 2026Article
- CXCL8 producing macrophages shape gastric cancer outcomes.Discover oncology · 2026Article
- Machine learning-enhanced QSAR modeling for predicting drug efficacy against the RET V804M kinase domain mutation.Molecular diversity · 2026Article
- Deep learning identifies TP-41 for methylglyoxal scavenging in Alzheimer's treatment.Theranostics · 2026Article
- Drug Repurposing in Glioblastoma Using a Machine Learning-Based Hybrid Feature Selection Approach.International journal of molecular sciences · 2025Article
- MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.BMC biology · 2025Article
- Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting.Briefings in bioinformatics · 2025Article
- SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction.bioRxiv : the preprint server for biology · 2025Article
- Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.PLoS computational biology · 2025Article
- DeepKinome: quantitative prediction of kinase binding affinity by a compound using deep learning based regression model.Frontiers in molecular biosciences · 2025Article
- The chemopreventive effects of native Brazilian plants on stomach cancer: A review of the last 25 years.Oncoscience · 2025Review
- Ligand-Based Compound Activity Prediction via Few-Shot Learning.Journal of chemical information and modeling · 2024Article
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- Novel prognostic signature for lung adenocarcinoma based on immune-related mRNA pairs.Heliyon · 2024Article
- Graph neural networks are promising for phenotypic virtual screening on cancer cell lines.Biology methods & protocols · 2024Article
- Pharmacogenomics and Big Data in medical oncology: developments and challenges.Therapeutic advances in medical oncology · 2024Review
- Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models.Cancers · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecular "fingerprints", along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.