deep learning designer Search Results


deep-learning model designed for breast cancer risk assessment  (Mirai INC)
90
Mirai INC deep-learning model designed for breast cancer risk assessment
Deep Learning Model Designed For Breast Cancer Risk Assessment, 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+designer/pmc10848404-57-6-14?v=Mirai+INC
Average 90 stars, based on 1 article reviews
deep-learning model designed for breast cancer risk assessment - by Bioz Stars, 2026-08
90/100 stars
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deep reinforcement learning based molecular de novo design method  (AstraZeneca ltd)
90
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
https://www.bioz.com/product/deep+learning+designer/pmc10637774-108-8-21?v=AstraZeneca+ltd
Average 90 stars, based on 1 article reviews
deep reinforcement learning based molecular de novo design method - by Bioz Stars, 2026-08
90/100 stars
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automated design and optimization of multitarget schizophrenia drug candidates  (MultiTarget Pharmaceuticals)
90
MultiTarget Pharmaceuticals automated design and optimization of multitarget schizophrenia drug candidates
( 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.
Automated Design And Optimization Of Multitarget Schizophrenia Drug Candidates, supplied by MultiTarget Pharmaceuticals, 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+designer/pm39408987-2061-0-5?v=MultiTarget+Pharmaceuticals
Average 90 stars, based on 1 article reviews
automated design and optimization of multitarget schizophrenia drug candidates - by Bioz Stars, 2026-08
90/100 stars
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( 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