Active learning using Generative Adversarial Networks for improving generalization and avoiding distractor points

Lim, Heechul; Chon, Kang-Wook; Kim, Min-Soo

doi:10.1016/j.eswa.2023.120193

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Regression analysis MLR [35] DT [36] LR [37] Classification SVM [38] CNN [39] RNN [40] GAN [41] Unsupervised learning…”

Section: Supervised Learningmentioning

confidence: 99%

“…A number of studies have also shown that GANs can be used for multi-objective drug designing, where the goal is to optimize multiple drug properties simultaneously [41]. By adjusting the loss function of the GAN, researchers can balance various factors, such as the binding affinity, solubility, and selectivity, to generate molecules with desirable multi-objective profiles [41,89]. It should be noted that previous studies [92] have indicated that GANs can optimize specific molecular properties, such as the lipophilicity, molecular weight, or solubility.…”

Section: (V) Text Miningmentioning

confidence: 99%

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Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Visan,

Negut

2024

Life

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Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI’s role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

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“…Regression analysis MLR [35] DT [36] LR [37] Classification SVM [38] CNN [39] RNN [40] GAN [41] Unsupervised learning…”

Section: Supervised Learningmentioning

confidence: 99%

Section: (V) Text Miningmentioning

confidence: 99%

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Visan,

Negut

2024

Life

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IndusSynthe: Synthetic data using human-machine intelligence hybrid for enhanced industrial surface defect detection through self-updating with multi-view filtering

Gong,

Liu,

Wang

2024

Advanced Engineering Informatics

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Label-free model evaluation and weighted uncertainty sample selection for domain adaptive instance segmentation

Guan,

Yuan

2024

Engineering Applications of Artificial Intelligence

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Active learning using Generative Adversarial Networks for improving generalization and avoiding distractor points

Cited by 3 publications

References 10 publications

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

IndusSynthe: Synthetic data using human-machine intelligence hybrid for enhanced industrial surface defect detection through self-updating with multi-view filtering

Label-free model evaluation and weighted uncertainty sample selection for domain adaptive instance segmentation

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