Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells-Reference-Cited by-同舟云学术

Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells

Published:2019-02-25 Issue: Volume: Page:
ISSN:1792-1074
Container-title:Oncology Letters
language:
Short-container-title:Oncol Lett

Author:

Ancuceanu Robert¹,Dinu Mihaela¹,Neaga Iana²,Laszlo Fekete³,Boda Daniel⁴

Affiliation:

1. Department of Pharmaceutical Botany and Cell Biology, Faculty of Pharmacy, ‘Carol Davila’ University of Medicine and Pharmacy, 020956 Bucharest, Romania

2. Department of Public Health and Management, Faculty of Medicine, ‘Carol Davila’ University of Medicine and Pharmacy, 050463 Bucharest, Romania

3. Department of Dermatology, University of Medicine and Pharmacy of T�rgu Mureş, 540142 T�rgu Mureş, Romania

4. Dermatology Research Laboratory, ‘Carol Davila’ University of Medicine and Pharmacy, 050474 Bucharest, Romania

Publisher

Spandidos Publications

Subject

Cancer Research,Oncology

Cited by 34 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter;Journal of Chemical Information and Modeling;2024-06-26

2. Prediction of apoptosis signal-regulating kinase 1 (ASK1) inhibition with machine learning methods;Chemical Papers;2024-05-14

3. Caramel: A web-based QSAR tool for melanoma drug discovery;Software Impacts;2024-03

4. A counterfactual analysis of opioid-involved deaths during the COVID-19 pandemic using a spatiotemporal random forest modeling approach;Health & Place;2023-03

5. Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework;Molecules;2023-01-31