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6 papers · ranked by Valyu relevance

01
CchemRxiv9 Dec 2024
Open-source and FAIR Research Software for Proteomics
Authors not listed
Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms…
72relevance
02
CchemRxiv29 Sep 2025
A Scalable Microservices Platform for Deploying Machine Learning Models in Drug Discovery and Beyond
Authors not listed
Machine learning models are transforming data-driven research across scientific disciplines, yet their deployment as accessible and reliable web services remains a significant challenge. We introduce the NERDD framework, a scalable, maintainable, and secure microservices platform designed to support the sustainable…
55relevance
03
CchemRxiv13 Jun 2022
CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.
Ruben Sanchez-Garcia, Dávid Havasi, Gergely Takács, Matthew C. Robinson + 3 more
Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available…
50relevance
04
CchemRxiv26 Jun 2022
CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.
Ruben Sanchez-Garcia, Dávid Havasi, Gergely Takács, Matthew C. Robinson + 3 more
Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available…
50relevance
05
CchemRxiv29 Jun 2022
CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.
Ruben Sanchez-Garcia, Dávid Havasi, Gergely Takács, Matthew C. Robinson + 3 more
Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available…
50relevance
06
CchemRxiv30 Jun 2022
CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.
Ruben Sanchez-Garcia, Dávid Havasi, Gergely Takács, Matthew C. Robinson + 3 more
Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available…
50relevance

Results for code availability

Open-source and FAIR Research Software for Proteomics

A Scalable Microservices Platform for Deploying Machine Learning Models in Drug Discovery and Beyond

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.

Open-source and FAIR Research Software for Proteomics

A Scalable Microservices Platform for Deploying Machine Learning Models in Drug Discovery and Beyond

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.