deep learning (dl)-based methods Search Results


deep learning (dl) algorithm based on 2d u-net (myomics-t1 software, version 1.0.0)  (Myomics Inc)
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Myomics Inc deep learning (dl) algorithm based on 2d u-net (myomics-t1 software, version 1.0.0)
Deep Learning (Dl) Algorithm Based On 2d U Net (Myomics T1 Software, Version 1.0.0), supplied by Myomics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+learning+%28dl%29-based+methods/pmc09747268-38-8-10?v=Myomics+Inc
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mammography-based deep learning (dl) model  (Mirai INC)
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Mirai INC mammography-based deep learning (dl) model
Mammography Based Deep Learning (Dl) Model, supplied by Mirai INC, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+learning+%28dl%29-based+methods/pmc10698602-43-4-9?v=Mirai+INC
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mammography-based deep learning (dl) model - by Bioz Stars, 2026-08
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deep learning-based methods  (Oxford Nanopore)
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Oxford Nanopore deep learning-based methods
Deep Learning Based Methods, supplied by Oxford Nanopore, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep learning-based methods - by Bioz Stars, 2026-08
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huanglongbing detection method for orange trees based on deep neural networks and transfer learning  (IEEE Access)
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IEEE Access huanglongbing detection method for orange trees based on deep neural networks and transfer learning
Huanglongbing Detection Method For Orange Trees Based On Deep Neural Networks And Transfer Learning, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+learning+%28dl%29-based+methods/pmc10716766-7-26-28?v=IEEE+Access
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huanglongbing detection method for orange trees based on deep neural networks and transfer learning - by Bioz Stars, 2026-08
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deep reinforcement learning based molecular de novo design method  (AstraZeneca ltd)
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AstraZeneca ltd deep reinforcement learning based molecular de novo design method
( A ) The identification of ten commercial-accessible aromatic fragments aided by deep <t>reinforcement</t> learning model; ( B ) Synthesis of LFS-1107 via the installation of aromatic tetrazole moiety selected from the previous step to the sulforaphene parent structure; ( C ) Assessment of protein-ligand binding kinetics and binding affinity of tetrazole aromatic fragments via Bio-layer interferometry (BLI) assay; ( D ) Binding affinity of LFS-1107 and KPT-330 determined via BLI assay: LFS-1107, K d ~1.25E-11 M; KPT-330: K d ~5.29E-09 M. Figure 1—source data 1. The chemical structure of 10 commercial-accessible aromatic fragments. Figure 1—source data 2. The synthesis of compound LFS-1107. Figure 1—source data 3. The data of affinities and binding kinetics of CRM1 to S5 and S8. Figure 1—source data 4. The data of affinities and binding kinetics of CRM1 to LFS-1107 and KPT-330.
Deep Reinforcement Learning Based Molecular De Novo Design Method, supplied by AstraZeneca ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep reinforcement learning based molecular de novo design method - by Bioz Stars, 2026-08
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deep learning-based spectral ct imaging (dl-scti) aquilion one genesis  (Canon inc)
90
Canon inc deep learning-based spectral ct imaging (dl-scti) aquilion one genesis
( A ) The identification of ten commercial-accessible aromatic fragments aided by deep <t>reinforcement</t> learning model; ( B ) Synthesis of LFS-1107 via the installation of aromatic tetrazole moiety selected from the previous step to the sulforaphene parent structure; ( C ) Assessment of protein-ligand binding kinetics and binding affinity of tetrazole aromatic fragments via Bio-layer interferometry (BLI) assay; ( D ) Binding affinity of LFS-1107 and KPT-330 determined via BLI assay: LFS-1107, K d ~1.25E-11 M; KPT-330: K d ~5.29E-09 M. Figure 1—source data 1. The chemical structure of 10 commercial-accessible aromatic fragments. Figure 1—source data 2. The synthesis of compound LFS-1107. Figure 1—source data 3. The data of affinities and binding kinetics of CRM1 to S5 and S8. Figure 1—source data 4. The data of affinities and binding kinetics of CRM1 to LFS-1107 and KPT-330.
Deep Learning Based Spectral Ct Imaging (Dl Scti) Aquilion One Genesis, supplied by Canon inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep learning-based spectral ct imaging (dl-scti) aquilion one genesis - by Bioz Stars, 2026-08
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Image Search Results


( A ) The identification of ten commercial-accessible aromatic fragments aided by deep reinforcement learning model; ( B ) Synthesis of LFS-1107 via the installation of aromatic tetrazole moiety selected from the previous step to the sulforaphene parent structure; ( C ) Assessment of protein-ligand binding kinetics and binding affinity of tetrazole aromatic fragments via Bio-layer interferometry (BLI) assay; ( D ) Binding affinity of LFS-1107 and KPT-330 determined via BLI assay: LFS-1107, K d ~1.25E-11 M; KPT-330: K d ~5.29E-09 M. Figure 1—source data 1. The chemical structure of 10 commercial-accessible aromatic fragments. Figure 1—source data 2. The synthesis of compound LFS-1107. Figure 1—source data 3. The data of affinities and binding kinetics of CRM1 to S5 and S8. Figure 1—source data 4. The data of affinities and binding kinetics of CRM1 to LFS-1107 and KPT-330.

Journal: eLife

Article Title: Discovery and biological evaluation of a potent small molecule CRM1 inhibitor for its selective ablation of extranodal NK/T cell lymphoma

doi: 10.7554/eLife.80625

Figure Lengend Snippet: ( A ) The identification of ten commercial-accessible aromatic fragments aided by deep reinforcement learning model; ( B ) Synthesis of LFS-1107 via the installation of aromatic tetrazole moiety selected from the previous step to the sulforaphene parent structure; ( C ) Assessment of protein-ligand binding kinetics and binding affinity of tetrazole aromatic fragments via Bio-layer interferometry (BLI) assay; ( D ) Binding affinity of LFS-1107 and KPT-330 determined via BLI assay: LFS-1107, K d ~1.25E-11 M; KPT-330: K d ~5.29E-09 M. Figure 1—source data 1. The chemical structure of 10 commercial-accessible aromatic fragments. Figure 1—source data 2. The synthesis of compound LFS-1107. Figure 1—source data 3. The data of affinities and binding kinetics of CRM1 to S5 and S8. Figure 1—source data 4. The data of affinities and binding kinetics of CRM1 to LFS-1107 and KPT-330.

Article Snippet: In the present study, we adopted the deep reinforcement learning based molecular de novo design method developed by Olivecrona etc. from AstraZeneca.

Techniques: Ligand Binding Assay, Binding Assay