A molecular generative model of ADAM10 inhibitors by using GRU-based deep neural network and transfer learning

Shi, Tingting; Huang, Shuheng; Chen, Linxin; Heng, Yu; Kuang, Zuyin; Xu, Lei; Mei, Hu

doi:10.1016/j.chemolab.2020.104122

Cited by 17 publications

(4 citation statements)

References 19 publications

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“…Bu çalışmada, GRU tabanlı generatif modelin, moleküllerin SMILES dizilerini doğru bir şekilde öğrenebildiği ve yeni potansiyel ADAM10 inhibitörleri üretebildiği gösterilmiştir. Geleneksel ligand bazlı yöntemlerle kıyaslandığında, GRU tabanlı model, kimyasal ligandların yalnızca SMILES bilgilerini gerektirmekte ve etkili geniş bir seri potansiyel yeni bileşikler oluşturabilmektedir [184].…”

Section: De Novo İlaç Tasarımında Yapay Zeka Uygulamasıunclassified

İlaç Tasariminda Yapay Zekâ Uygulamalari

TARI,

ARPACI

2023

Ankara Ecz. Fak. Derg.

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Amaç: Yapay zekâ üzerindeki çalışmaların giderek artması, her alanda olduğu gibi ilaç endüstrisinin de bu çalışmalardan faydalanmasına sebep olmaktadır. Bu çalışmada, yapay zeka uygulamalarının ilaç tasarımı ve geliştirilmesi üzerinde nasıl bir rol aldığının incelenmesi amaçlanmıştır. Sonuç ve Tartışma: Yeni biyolojik olarak aktif bileşiklere ihtiyacın giderek arttığı günümüzde, yapay zekada sürekli yeni algoritmaların ortaya çıkması, güçlü hesaplama yeteneği, elde edilen kimyasal ve biyolojik verilerin birikmesi, ilaç tasarımında yapay zekâ kullanımına olanak sunmaktadır. İlaç tasarım aşamalarının neredeyse tüm basamaklarında uygulanabilen yapay zekâ yöntemleriyle, yeni ilaç geliştirilmesindeki uzun zaman gereksinimi ve yüksek maliyet gibi zorluklar azaltılmaya çalışılmaktadır. Bu çalışma sonucunda, yapay zekâ teknolojisinin ilaç tasarım sürecindeki uygulamaları ve geleneksel yöntemlere göre avantajları kapsamlı bir şekilde analiz edilerek karşılaştırılmıştır.

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Section: De Novo İlaç Tasarımında Yapay Zeka Uygulamasıunclassified

İlaç Tasariminda Yapay Zekâ Uygulamalari

TARI,

ARPACI

2023

Ankara Ecz. Fak. Derg.

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“…Recurrent neural networks such as LSTM and GRU are commonly used in problems in which sequence and correlation between data entries are relevant, namely in timeseries forecasting or sequence generation [40] [41] [42]. This happens due to gates in the structure of those networks that utilize previous data entries to influence next predictions while update themselves.…”

Section: A Gated Recurrent Unit Neural Networkmentioning

confidence: 99%

A Gated Recurrent Unit Deep Learning Model to Detect and Mitigate Distributed Denial of Service and Portscan Attacks

et al. 2022

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Nowadays, it is common for applications to require servers to run constantly and aim as close as possible to zero downtime. The slightest failure might cause significant financial losses and sometimes even lives. For this reason, security and management measures against network threats are fundamental and have been researched for years. Software-defined networks (SDN) are an advancement in network management due to their centralization of the control plane, as it facilitates equipment setup and administration over the local network. However, this centralization makes the controller a target to denial of service attacks (DoS). In this study, we aim to develop a network anomaly detection and mitigation system that uses gated recurrent unit (GRU) neural networks combined with fuzzy logic. The neural network is trained to forecast future traffic, and anomalies are detected when the forecasting fails. The system is designed to operate in software-defined networks since they provide network flow information and tools to manage forwarding tables. We also demonstrate how the neural network's hyperparameters affect the detection module. The system was tested using two datasets: one with emulated traffic generated by the data communication and networking research group called Orion, from computer science department at state university of Londrina, and CICDDoS2019, a well-known dataset by the anomaly detection community. The results show that GRU networks combined with fuzzy logic are a viable option to detect anomalies in SDN and possibly in other anomaly detection applications. We compared our model with other deep learning techniques.

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“…arated reagents. In addition to innovations in the model structure, many strategies can assist AI in better comprehending chemical reactions, including data augmentation 26 and transfer learning, 27 which have shown satisfactory functions in tackling low-chemical-data regimes. However, the discovery of new reactions by automatically extracting rules from known chemical reactions is an arduous process.…”

Section: Cluster Synlettmentioning

confidence: 99%

A Novel Application of a Generation Model in Foreseeing ‘Future’ Reactions

Cao

Zhuang

et al. 2022

Synlett

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Deep learning is widely used in chemistry and can rival human chemists in certain scenarios. Inspired by molecule generation in new drug discovery, we present a deep learning-based reaction generation approach to perform reaction generation with the Trans-VAE model in this study. To comprehend how exploratory and innovative the model is in reaction generation, we constructed the dataset by time-split. We applied the Michael addition reaction as the generation vehicle and took the reactions reported before a certain date as the training set and explored whether the model could generate reactions that were reported after the date. We took 2010 and 2015 as the time points for the splitting of the Michael addition reaction respectively. Among the generated reactions, 911 and 487 reactions were applied in the experiments after the respective split time points, accounting for 12.75% and 16.29% of all reported reactions after each time point. The generated results were in line with expectations and additionally generated a large quantity of new chemically feasible Michael addition reactions, which also demonstrated the learnability of the Trans-VAE model for reaction rules. Our research provides a reference for future novel reaction discovery using deep learning.

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A molecular generative model of ADAM10 inhibitors by using GRU-based deep neural network and transfer learning

Cited by 17 publications

References 19 publications

İlaç Tasariminda Yapay Zekâ Uygulamalari

İlaç Tasariminda Yapay Zekâ Uygulamalari

A Gated Recurrent Unit Deep Learning Model to Detect and Mitigate Distributed Denial of Service and Portscan Attacks

A Novel Application of a Generation Model in Foreseeing ‘Future’ Reactions

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