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Molecular Pharmaceutics
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August 1, 2018
Prototype-Based Compound Discovery Using Deep Generative Models
Shahar Harel, Kira Radinsky
JMIR AI
|
October 2, 2024
Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study
Shunit Agmon, Uriel Singer, Kira Radinsky
Bioinformatics (Oxford, England)
|
December 16, 2020
On biases of attention in scientific discovery
Uriel Singer, Kira Radinsky, Eric Horvitz
Bioinformatics (Oxford, England)
|
August 12, 2025
Beyond the leaderboard: leveraging predictive modeling for protein-ligand insights and discovery
Dan Kalifa, Kira Radinsky, Eric Horvitz
Bioinformatics (Oxford, England)
|
May 5, 2026
Learning Protein Representations with Conformational Dynamics
Dan Kalifa, Eric Horvitz, Kira Radinsky
Expert Review of Clinical Pharmacology
|
June 25, 2019
Clinical pharmacology of old age
Gideon Koren, Galia Nordon, Kira Radinsky, et al.
Journal of Chemical Information and Modeling
|
May 28, 2025
Predicting Oxidation Potentials with DFT-Driven Machine Learning
Shweta Sharma, Natan Kaminsky, Kira Radinsky, et al.
Clinical Drug Investigation
|
March 15, 2019
Chronic Use of β-Blockers and the Risk of Parkinson's Disease
Gideon Koren, Galia Norton, Kira Radinsky, et al.
Pharmacology Research & Perspectives
|
November 26, 2019
Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning
Gideon Koren, Galia Nordon, Kira Radinsky, et al.
Pharmacology Research & Perspectives
|
May 4, 2018
Machine learning of big data in gaining insight into successful treatment of hypertension
Gideon Koren, Galia Nordon, Kira Radinsky, et al.
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of 2
Search research articles
Search
Showing results (1-10 of 17) with videos related to
Sort By:
Page
of 2
Molecular Pharmaceutics
|
August 1, 2018
Prototype-Based Compound Discovery Using Deep Generative Models
Shahar Harel, Kira Radinsky
JMIR AI
|
October 2, 2024
Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study
Shunit Agmon, Uriel Singer, Kira Radinsky
Bioinformatics (Oxford, England)
|
December 16, 2020
On biases of attention in scientific discovery
Uriel Singer, Kira Radinsky, Eric Horvitz
Bioinformatics (Oxford, England)
|
August 12, 2025
Beyond the leaderboard: leveraging predictive modeling for protein-ligand insights and discovery
Dan Kalifa, Kira Radinsky, Eric Horvitz
Bioinformatics (Oxford, England)
|
May 5, 2026
Learning Protein Representations with Conformational Dynamics
Dan Kalifa, Eric Horvitz, Kira Radinsky
Expert Review of Clinical Pharmacology
|
June 25, 2019
Clinical pharmacology of old age
Gideon Koren, Galia Nordon, Kira Radinsky, et al.
Journal of Chemical Information and Modeling
|
May 28, 2025
Predicting Oxidation Potentials with DFT-Driven Machine Learning
Shweta Sharma, Natan Kaminsky, Kira Radinsky, et al.
Clinical Drug Investigation
|
March 15, 2019
Chronic Use of β-Blockers and the Risk of Parkinson's Disease
Gideon Koren, Galia Norton, Kira Radinsky, et al.
Pharmacology Research & Perspectives
|
November 26, 2019
Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning
Gideon Koren, Galia Nordon, Kira Radinsky, et al.
Pharmacology Research & Perspectives
|
May 4, 2018
Machine learning of big data in gaining insight into successful treatment of hypertension
Gideon Koren, Galia Nordon, Kira Radinsky, et al.
Page
of 2