ArticleInternational journal of molecular sciences2020
Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches.Prostate cancer and prostatic diseases · 2022Pooled it
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- Real-world effectiveness of darolutamide in metastatic castration-resistant prostate cancer.Endocrine-related cancer · 2025Article
- Androgen receptor signalling in non-prostatic malignancies: challenges and opportunities.Nature reviews. Cancer · 2025Review
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists.Journal of cheminformatics · 2023Article
- Simulating androgen receptor selection in designer yeast.Synthetic and systems biotechnology · 2022Article
- Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer.International journal of general medicine · 2021Article
- Prediction of Protein-ligand Interaction Based on Sequence Similarity and Ligand Structural Features.International journal of molecular sciences · 2020Article
- Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well as for designing drugs that do not elicit adverse responses. However, experimental characterization of these mutations is time consuming and costly; thus, predictive models are needed to anticipate resistant mutations and to guide the drug discovery process. In this work, we leverage experimental data collected on 68 AR mutants, either observed in the clinic or described in the literature, to train a deep neural network (DNN) that predicts the response of these mutants to currently used and experimental anti-androgens and testosterone. We demonstrate that the use of this DNN, with general 2D descriptors, provides a more accurate prediction of the biological outcome (inhibition, activation, no-response, mixed-response) in AR mutant-drug pairs compared to other machine learning approaches. Finally, the developed approach was used to make predictions of AR mutant response to the latest AR inhibitor darolutamide, which were then validated by in-vitro experiments.
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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.