Advanced search
Publication Cover
SAR and QSAR in Environmental Research Volume 30, 2019 - Issue 9: Special Issue: 10th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (CMTPI 2019). Part 1. Guest Editors: J. Devillers and A. Geronikaki
Submit an article Journal homepage
748
Views
27
CrossRef citations to date
0
Altmetric
Articles

Could deep learning in neural networks improve the QSAR models?

G. GiniDEIB, Politecnico di Milano, Milan, ItalyCorrespondence[email protected]
,
F. ZanoliDEIB, Politecnico di Milano, Milan, Italy
,
A. GambaIstituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy
,
G. RaitanoIstituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy
&
E. BenfenatiIstituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy
Pages 617-642 | Received 25 Jun 2019, Accepted 29 Jul 2019, Published online: 28 Aug 2019

Citations (27)

Keep up to date with the latest research on this topic with citation updates for this article.

Subscribe to citation updates

Read on this site (1)

S. Nandi, P. Kumar, S.A. Amin, T. Jha & S. Gayen. (2021) First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis. SAR and QSAR in Environmental Research 32:11, pages 917-939.
Read now

Articles from other publishers (26)

Nataša Sukur, Nemanja Milošević, Kristina Pogrmic-Majkic, Bojana Stanic & Nebojsa Andric. (2023) Predicting chemicals’ toxicity pathway of female reproductive disorders using AOP7 and deep neural networks. Food and Chemical Toxicology 180, pages 114013.
Crossref
Ananda Fitri Karimah, Angel Metanosa Afinda & Isman Kurniawan. (2023) Implementation of SMILES2Vec-based LSTM for Predicting Drug Side Effects: Case Study of Hepatobiliary Disorder. Implementation of SMILES2Vec-based LSTM for Predicting Drug Side Effects: Case Study of Hepatobiliary Disorder.
Maged Nasser, Umi Kalsom Yusof & Naomie Salim. (2023) Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review. Processes 11:5, pages 1340.
Crossref
Roi Naveiro, María J. Martínez, Axel J. Soto, Ignacio Ponzoni, David Ríos-Insua & Nuria E. Campillo. 2023. Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development. Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development 263 284 .
Giuseppina Gini. 2023. Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development. Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development 89 137 .
Giuseppina Gini, Chiakang Hung & Emilio Benfenati. 2023. Big Data Analytics in Chemoinformatics and Bioinformatics. Big Data Analytics in Chemoinformatics and Bioinformatics 115 150 .
Emilio Benfenati, Gianluca Selvestrel, Anna Lombardo & Davide Luciani. 2023. Current Trends in Computational Modeling for Drug Discovery. Current Trends in Computational Modeling for Drug Discovery 213 237 .
Linlin Zhao & Catrin Hasselgren. 2023. Machine Learning and Deep Learning in Computational Toxicology. Machine Learning and Deep Learning in Computational Toxicology 447 461 .
Huixiao Hong, Jie Liu, Weigong Ge, Sugunadevi Sakkiah, Wenjing Guo, Gokhan Yavas, Chaoyang Zhang, Ping Gong, Weida Tong & Tucker A. Patterson. 2023. Machine Learning and Deep Learning in Computational Toxicology. Machine Learning and Deep Learning in Computational Toxicology 297 321 .
María Jimena Martínez, María Virginia Sabando, Axel J. Soto, Carlos Roca, Carlos Requena-Triguero, Nuria E. Campillo, Juan A. Páez & Ignacio Ponzoni. (2022) Multitask Deep Neural Networks for Ames Mutagenicity Prediction. Journal of Chemical Information and Modeling 62:24, pages 6342-6351.
Crossref
Taiki Miyazawa, Yoichi Hiratsuka, Masako Toda, Nozomu Hatakeyama, Hitoshi Ozawa, Chizumi Abe, Ting-Yu Cheng, Yuji Matsushima, Yoshifumi Miyawaki, Kinya Ashida, Jun Iimura, Tomohiro Tsuda, Hiroto Bushita, Kazuichi Tomonobu, Satoshi Ohta, Hsuan Chung, Yusuke Omae, Takayuki Yamamoto, Makoto Morinaga, Hiroshi Ochi, Hajime Nakada, Kazuhiro Otsuka & Teruo Miyazawa. (2022) Artificial intelligence in food science and nutrition: a narrative review. Nutrition Reviews 80:12, pages 2288-2300.
Crossref
Hemkiran S. & Sudha Sadasivam G.. (2022) Prediction of Drug Bioactivity in Alzheimer’s Disease Using Machine Learning Techniques and Community Networks. Current Bioinformatics 17:8, pages 698-709.
Crossref
Chaofeng Lou, Hongbin Yang, Jiye Wang, Mengting Huang, Weihua Li, Guixia Liu, Philip W. Lee & Yun Tang. (2022) IDL-PPBopt: A Strategy for Prediction and Optimization of Human Plasma Protein Binding of Compounds via an Interpretable Deep Learning Method. Journal of Chemical Information and Modeling 62:11, pages 2788-2799.
Crossref
Mantas Vaškevičius, Jurgita Kapočiūtė-Dzikienė & Liudas Šlepikas. (2022) Modeling of the Crystallization Conditions for Organic Synthesis Product Purification Using Deep Learning. Electronics 11:9, pages 1360.
Crossref
B. Sepehri, R. Ghavami, S. Farahbakhsh & R. Ahmadi. (2021) Machine learning-based quantitative structure–retention relationship models for predicting the retention indices of volatile organic pollutants. International Journal of Environmental Science and Technology 19:3, pages 1457-1466.
Crossref
Cecile Valsecchi, Magda Collarile, Francesca Grisoni, Roberto Todeschini, Davide Ballabio & Viviana Consonni. (2020) Predicting molecular activity on nuclear receptors by multitask neural networks. Journal of Chemometrics 36:2.
Crossref
Giuseppina Gini. 2022. In Silico Methods for Predicting Drug Toxicity. In Silico Methods for Predicting Drug Toxicity 1 26 .
Giuseppina Gini & Emilio Benfenati. 2021. Chemometrics and Cheminformatics in Aquatic Toxicology. Chemometrics and Cheminformatics in Aquatic Toxicology 89 124 .
Yasunari Matsuzaka, Shin Totoki, Kentaro Handa, Tetsuyoshi Shiota, Kota Kurosaki & Yoshihiro Uesawa. (2021) Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure–Activity Relationship System. International Journal of Molecular Sciences 22:19, pages 10821.
Crossref
Chiakang Hung & Giuseppina Gini. (2021) QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction. Molecular Diversity 25:3, pages 1283-1299.
Crossref
David Prihoda, Julia M. Maritz, Ondrej Klempir, David Dzamba, Christopher H. Woelk, Daria J. Hazuda, Danny A. Bitton & Geoffrey D. Hannigan. (2021) The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability. Natural Product Reports 38:6, pages 1100-1108.
Crossref
Sharna-kay Daley & Geoffrey A. Cordell. (2021) Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs. Molecules 26:13, pages 3800.
Crossref
Sharna-kay Daley & Geoffrey A. Cordell. (2021) Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix. Natural Product Communications 16:3, pages 1934578X2110030.
Crossref
Noorain Khalifa, Leela Sarath Kumar Konda & Rajendra Kristam. (2020) Machine learning-based QSAR models to predict sodium ion channel (Na v 1.5) blockers . Future Medicinal Chemistry 12:20, pages 1829-1843.
Crossref
Giuseppina Gini. (2020) The QSAR similarity principle in the deep learning era: Confirmation or revision?. Foundations of Chemistry 22:3, pages 383-402.
Crossref
Dmitriy D. Matyushin & Aleksey K. Buryak. (2020) Gas Chromatographic Retention Index Prediction Using Multimodal Machine Learning. IEEE Access 8, pages 223140-223155.
Crossref

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.