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RSC Medicinal Chemistry
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October 30, 2025
Accelerating compound synthesis in drug discovery: the role of digitalisation and automation
David F Nippa, Alexander J Boddy, Kenneth Atz, et al.
Molecular Informatics
|
January 23, 2025
Simple User-Friendly Reaction Format
David F Nippa, Alex T Müller, Kenneth Atz, et al.
RSC Medicinal Chemistry
|
July 19, 2024
Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry
Kenneth Atz, David F Nippa, Alex T Müller, et al.
Communications Chemistry
|
November 21, 2023
Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening
David F Nippa, Kenneth Atz, Alex T Müller, et al.
Nature Chemistry
|
November 23, 2023
Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning
David F Nippa, Kenneth Atz, Remo Hohler, et al.
Nature Communications
|
January 17, 2025
Author Correction: Prospective de novo drug design with deep interactome learning
Kenneth Atz, Leandro Cotos, Clemens Isert, et al.
Nature Communications
|
April 22, 2024
Prospective de novo drug design with deep interactome learning
Kenneth Atz, Leandro Cotos, Clemens Isert, et al.
Nature Communications
|
November 25, 2025
Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization
David F Nippa, Kenneth Atz, Yannick Stenzhorn, et al.
Chimia
|
September 2, 2024
Enhancing Drug Discovery and Development through the Integration of Medicinal Chemistry, Chemical Biology, and Academia-Industry Partnerships: Insights from Roche's Endocannabinoid System Projects
Johannes Aebi, Kenneth Atz, Simon M Ametamey, et al.
Page
of 1
Search research articles
Search
Showing results (1-10 of 9) with videos related to
Sort By:
Page
of 1
RSC Medicinal Chemistry
|
October 30, 2025
Accelerating compound synthesis in drug discovery: the role of digitalisation and automation
David F Nippa, Alexander J Boddy, Kenneth Atz, et al.
Molecular Informatics
|
January 23, 2025
Simple User-Friendly Reaction Format
David F Nippa, Alex T Müller, Kenneth Atz, et al.
RSC Medicinal Chemistry
|
July 19, 2024
Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry
Kenneth Atz, David F Nippa, Alex T Müller, et al.
Communications Chemistry
|
November 21, 2023
Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening
David F Nippa, Kenneth Atz, Alex T Müller, et al.
Nature Chemistry
|
November 23, 2023
Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning
David F Nippa, Kenneth Atz, Remo Hohler, et al.
Nature Communications
|
January 17, 2025
Author Correction: Prospective de novo drug design with deep interactome learning
Kenneth Atz, Leandro Cotos, Clemens Isert, et al.
Nature Communications
|
April 22, 2024
Prospective de novo drug design with deep interactome learning
Kenneth Atz, Leandro Cotos, Clemens Isert, et al.
Nature Communications
|
November 25, 2025
Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization
David F Nippa, Kenneth Atz, Yannick Stenzhorn, et al.
Chimia
|
September 2, 2024
Enhancing Drug Discovery and Development through the Integration of Medicinal Chemistry, Chemical Biology, and Academia-Industry Partnerships: Insights from Roche's Endocannabinoid System Projects
Johannes Aebi, Kenneth Atz, Simon M Ametamey, et al.
Page
of 1