12 papers · ranked by Valyu relevance
Anselm W. Hahn, Joseph Zsombor-Pindera, Pierre Kennepohl, Serena DeBeer
In chemistry, analyzing spectra through peak fitting is a crucial task that helps scientists extract useful quantitative information about a sample's chemical composition or electronic structure. To make this process more efficient, we have developed a new open-source software tool called SpectraFit. This tool allows…
Oliver Lee, Malte Gather, Eli Zysman-Colman
We describe a new tool for the efficient management of computational chemistry. Digichem is a program that automates and simplifies nearly the entire computational pipeline, including large-scale batch submission of calculations, analysis and results parsing, the generation of 3D density plots and 2D graphs of…
Monika Vogler, Jonas Busk, Hamidreza Hajiyani, Peter Bjørn Jørgensen + 9 more
The future of materials science is borderless, cooperative, and distributed across the globe. This necessitates flexible, reconfigurable software defined research workflows, which we herein demonstrate by integrating multiple disciplines and modalities. Our brokering approach to research orchestration exposes entire…
Authors not listed
We present an open source collection of scripts and programs for the setup, management and evaluation of calculations with the Vienna ab-initio simulation package (VASP), called utils4VASP. It contains 20 independent Python scripts and Fortran programs, all with a unified and intuitive handling concept based on command…
Marta Pasquini, Marco Stenta
Background. The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction…
Raphaël Robidas, Claude Legault
Computational chemistry is an increasingly active field due to the improvement of computing resources and theoretical tools. However, its use remains usually limited to technically-inclined users due to the technical challenges of preparing, launching and analyzing calculations. In this context, we have developed…
Authors not listed
Predicting sites of metabolism (SOMs), i.e., the atom positions in a molecule where metabolic reactions are initiated, is an important component of the computational development pipeline for pharmaceuticals, agrochemicals, and cosmetics. Among the available SOM prediction models, FAME 3, published in 2019, stands out…
Himaghna Bhattacharjee, Jackson Burns, Dionisios Vlachos
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery…
Himaghna Bhattacharjee, Jackson Burns, Dionisios Vlachos
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery…
Himaghna Bhattacharjee, Jackson Burns, Dionisios Vlachos
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery…
Himaghna Bhattacharjee, Jackson Burns, Dionisios Vlachos
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery…
Authors not listed
This paper presents GLAS (Git-based Lab Automated Scheduler or Get Lab Automation Simplified), an open-source, robust, and highly expandable Git-based architecture designed for laboratory automation. GLAS can be deployed in both partially and fully automated experimental science laboratories, enabling the development…