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Sebastian Raschka

Showing results (1-10 of 10) with videos related to

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Current Opinion in Structural Biology|March 26, 2019
Automated discovery of GPCR bioactive ligandsSebastian Raschka
Methods (San Diego, Calif.)|July 10, 2020
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognitionSebastian Raschka, Benjamin Kaufman
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|September 21, 2020
PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face PrivacyVahid Mirjalili, Sebastian Raschka, Arun Ross
Methods in Molecular Biology (Clifton, N.J.)|September 7, 2023
Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot SpotsJiaxing Chen, Leslie A Kuhn, Sebastian Raschka
Proteins|October 5, 2016
Detecting the native ligand orientation by interfacial rigidity: SiteInterlockSebastian Raschka, Joseph Bemister-Buffington, Leslie A Kuhn
Journal of Computer-Aided Molecular Design|February 14, 2018
Protein-ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexesSebastian Raschka, Alex J Wolf, Joseph Bemister-Buffington, et al.
Biomolecules|March 19, 2020
Machine Learning to Identify Flexibility Signatures of Class A GPCR InhibitionJoseph Bemister-Buffington, Alex J Wolf, Sebastian Raschka, et al.
Methods in Molecular Biology (Clifton, N.J.)|March 30, 2018
Automated Inference of Chemical Discriminants of Biological ActivitySebastian Raschka, Anne M Scott, Mar Huertas, et al.
Journal of Computer-Aided Molecular Design|February 1, 2018
Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species controlSebastian Raschka, Anne M Scott, Nan Liu, et al.
Plos Computational Biology|March 24, 2022
Ten quick tips for deep learning in biologyBenjamin D Lee, Anthony Gitter, Casey S Greene, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Current Opinion in Structural Biology|March 26, 2019
Automated discovery of GPCR bioactive ligandsSebastian Raschka
Methods (San Diego, Calif.)|July 10, 2020
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognitionSebastian Raschka, Benjamin Kaufman
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|September 21, 2020
PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face PrivacyVahid Mirjalili, Sebastian Raschka, Arun Ross
Methods in Molecular Biology (Clifton, N.J.)|September 7, 2023
Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot SpotsJiaxing Chen, Leslie A Kuhn, Sebastian Raschka
Proteins|October 5, 2016
Detecting the native ligand orientation by interfacial rigidity: SiteInterlockSebastian Raschka, Joseph Bemister-Buffington, Leslie A Kuhn
Journal of Computer-Aided Molecular Design|February 14, 2018
Protein-ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexesSebastian Raschka, Alex J Wolf, Joseph Bemister-Buffington, et al.
Biomolecules|March 19, 2020
Machine Learning to Identify Flexibility Signatures of Class A GPCR InhibitionJoseph Bemister-Buffington, Alex J Wolf, Sebastian Raschka, et al.
Methods in Molecular Biology (Clifton, N.J.)|March 30, 2018
Automated Inference of Chemical Discriminants of Biological ActivitySebastian Raschka, Anne M Scott, Mar Huertas, et al.
Journal of Computer-Aided Molecular Design|February 1, 2018
Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species controlSebastian Raschka, Anne M Scott, Nan Liu, et al.
Plos Computational Biology|March 24, 2022
Ten quick tips for deep learning in biologyBenjamin D Lee, Anthony Gitter, Casey S Greene, et al.
Pageof 1