ArticleNucleic acids research2020
PaccMann: a web service for interpretable anticancer compound sensitivity prediction.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- In silico discovery of natural compounds from Vietnamese essential oils database with target binding to cancer-related proteins.Molecular diversity · 2026Article
- Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.iScience · 2026Article
- Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning.Bioinformatics (Oxford, England) · 2026Article
- Machine learning-assisted screening of natural product database for the identification of novel chalcone-based derivative as a potent DHODH inhibitor in cancer therapy.Discover oncology · 2026Article
- Exploring graph-based models for predicting active compounds against triple-negative breast cancer.Molecular diversity · 2026Article
- Discovering TEAD4 modulators for hepatocellular carcinoma: a GAN-enabled generative modelling framework.Frontiers in bioinformatics · 2026Article
- PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response.Frontiers in pharmacology · 2026Article
- Evaluation of selected indigenous spices- and herbs-derived small molecules as potential inhibitors of SREBP and its implications for breast cancer using MD simulations and MMPBSA calculations.Molecular diversity · 2025Article
- In Silico and In Vitro Evaluation of δ-cadinene fromCancers · 2025Article
- Integrating artificial intelligence in drug discovery and early drug development: a transformative approach.Biomarker research · 2025Review
- Identifying Potential Autophagy Modulators in Panch Phoron Spices (P5S): An In Silico approach.ACS omega · 2025Article
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
- Learning chemical sensitivity reveals mechanisms of cellular response.Communications biology · 2024Article
- shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning.Patterns (New York, N.Y.) · 2024Article
- Article
- Graph neural networks are promising for phenotypic virtual screening on cancer cell lines.Biology methods & protocols · 2024Article
- Article
- Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models.Cancers · 2023Article
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
- CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
The identification of new targeted and personalized therapies for cancer requires the fast and accurate assessment of the drug efficacy of potential compounds against a particular biomolecular sample. It has been suggested that the integration of complementary sources of information might strengthen the accuracy of a drug efficacy prediction model. Here, we present a web-based platform for the Prediction of AntiCancer Compound sensitivity with Multimodal Attention-based Neural Networks (PaccMann). PaccMann is trained on public transcriptomic cell line profiles, compound structure information and drug sensitivity screenings, and outperforms state-of-the-art methods on anticancer drug sensitivity prediction. On the open-access web service (https://ibm.biz/paccmann-aas), users can select a known drug compound or design their own compound structure in an interactive editor, perform in-silico drug testing and investigate compound efficacy on publicly available or user-provided transcriptomic profiles. PaccMann leverages methods for model interpretability and outputs confidence scores as well as attention heatmaps that highlight the genes and chemical sub-structures that were more important to make a prediction, hence facilitating the understanding of the model's decision making and the involved biochemical processes. We hope to serve the community with a toolbox for fast and efficient validation in drug repositioning or lead compound identification regimes.
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