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David F Nippa

Showing results (1-10 of 9) with videos related to

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RSC Medicinal Chemistry|October 30, 2025
Accelerating compound synthesis in drug discovery: the role of digitalisation and automationDavid F Nippa, Alexander J Boddy, Kenneth Atz, et al.
Molecular Informatics|January 23, 2025
Simple User-Friendly Reaction FormatDavid 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 chemistryKenneth 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 screeningDavid 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 learningDavid F Nippa, Kenneth Atz, Remo Hohler, et al.
Nature Communications|January 17, 2025
Author Correction: Prospective de novo drug design with deep interactome learningKenneth Atz, Leandro Cotos, Clemens Isert, et al.
Nature Communications|April 22, 2024
Prospective de novo drug design with deep interactome learningKenneth 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 optimizationDavid 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 ProjectsJohannes Aebi, Kenneth Atz, Simon M Ametamey, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
RSC Medicinal Chemistry|October 30, 2025
Accelerating compound synthesis in drug discovery: the role of digitalisation and automationDavid F Nippa, Alexander J Boddy, Kenneth Atz, et al.
Molecular Informatics|January 23, 2025
Simple User-Friendly Reaction FormatDavid 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 chemistryKenneth 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 screeningDavid 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 learningDavid F Nippa, Kenneth Atz, Remo Hohler, et al.
Nature Communications|January 17, 2025
Author Correction: Prospective de novo drug design with deep interactome learningKenneth Atz, Leandro Cotos, Clemens Isert, et al.
Nature Communications|April 22, 2024
Prospective de novo drug design with deep interactome learningKenneth 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 optimizationDavid 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 ProjectsJohannes Aebi, Kenneth Atz, Simon M Ametamey, et al.
Pageof 1