ArticleMolecular pharmaceutics2016
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.
Article in Molecular pharmaceutics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 196 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
196 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.Journal of nanobiotechnology · 2026Pooled it
- A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.Nature biotechnology · 2025Trial
- scBalFlow: a staged flow matching framework for imbalanced single-cell drug perturbation prediction.Bioinformatics (Oxford, England) · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Artificial Intelligence-Enabled Exosomes in Precision Oncology: A Framework for Clinical Utility and Biomedical Applications.Current issues in molecular biology · 2026Review
- Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2026Review
- KG-bench: benchmarking graph neural network algorithms for drug repurposing.Bioinformatics (Oxford, England) · 2026Article
- Drug repurposing against viral infections (2020-2025): clinical trials, computational strategies, and therapeutic interventions.Inflammopharmacology · 2026Review
- Graph-Aware AURALSTM: An Attentive Unified Representation Architecture with BiLSTM for Enhanced Molecular Property Prediction.Molecular diversity · 2026Article
- ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing.Scientific reports · 2026Article
- A new paradigm for retroperitoneal leiomyosarcoma: integrating transcriptomic subtyping and surgical risk stratification for AI-guided drug repurposing.Oncology reviews · 2026Review
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- Artificial intelligence-driven screening, early diagnosis, and treatment strategies for cervical cancer: an overview.Infectious agents and cancer · 2025Review
- A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development.Journal of medicinal chemistry · 2025Article
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- AI in drug repurposing for cancer therapies.Annals of medicine and surgery (2012) · 2025Article
- Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.RSC medicinal chemistry · 2025Review
- Leveraging Artificial Intelligence for Advancements in Liquid Dosage Formulations in the Pharmaceutical Industry.Therapeutic innovation & regulatory science · 2025Review
- Advances and challenges in drug repurposing in precision therapeutics of colorectal cancer.World journal of gastrointestinal oncology · 2025Review
- Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors.Nature communications · 2025Article
136 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.
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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.