Digital Discovery, 2023, 2,1471-1483
DOI: 10.1039/D3DD00105A, Paper

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Kiran Vaddi, Karen Li, Lilo D. Pozzo
We present an automated method to extract phase maps from experimental data that is of the functional form (e.g.: spectroscopy, scattering, diffraction) using the notion of shape distance between two curves represented as one dimensional functions.
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Digital Discovery, 2023, 2,1289-1296
DOI: 10.1039/D3DD00039G, Paper

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Markus Orsi, Daniel Probst, Philippe Schwaller, Jean-Louis Reymond
Reaction informatics is used to map the chemical space of drugs paired by similarity according to different molecular fingerprints.
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Digital Discovery, 2023, 2,1484-1493
DOI: 10.1039/D3DD00107E, Paper

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Jiaquan Huang, Qiandi Gao, Ying Tang, Yaxin Wu, Heqian Zhang, Zhiwei Qin
Utilizing a large protein language model, we have formulated a deep learning framework designed for predicting type II polyketide natural products.
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Digital Discovery, 2023, 2,1494-1505
DOI: 10.1039/D3DD00098B, Paper

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Edgar López-López, Oscar Robles, Fabien Plisson, José L. Medina-Franco
Peptide structure–activity/property relationship (P-SA/PR) studies focus on understanding how the structural variations of peptides influence their biological activities and other functional properties.
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Digital Discovery, 2023, 2,1558-1564
DOI: 10.1039/D3DD00121K, Paper

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Anthony Onwuli, Ashish V. Hegde, Kevin V. T. Nguyen, Keith T. Butler, Aron Walsh
Elements can be represented as vectors in a high-dimensional chemical space. We explore the distance and correlation between these vectors for different machine learning models.
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Digital Discovery, 2023, 2,1565-1576
DOI: 10.1039/D3DD00091E, Paper

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Arsalan Hashemi, Reza Khakpour, Amir Mahdian, Michael Busch, Pekka Peljo, Kari Laasonen
Computational high-throughput is used to evaluate proton–electron transfer reactions of quinone-type compounds that are potentially useful for energy storage.
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Digital Discovery, 2023, 2,1577-1588
DOI: 10.1039/D3DD00144J, Paper

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Koichi Handa, Sakae Sugiyama, Michiharu Kageyama, Takeshi Iijima
To precisely predict the intestinal absorption ratio (Fa) at an early stage in the discovery, we combined a data-driven (using chemical structures) and mechanism-based approach (using gastrointestinal unified theoretical framework).
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Digital Discovery, 2023, Advance Article
DOI: 10.1039/D3DD00166K, Paper

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Dan Guevarra, Kevin Kan, Yungchieh Lai, Ryan J. R. Jones, Lan Zhou, Phillip Donnelly, Matthias Richter, Helge S. Stein, John M. Gregoire
Human researchers multi-task, collaborate, and share resources. HELAO-async is a multi-workflow automation software that helps realize these attributes in materials acceleration platforms.
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Digital Discovery, 2023, Advance Article
DOI: 10.1039/D3DD00099K, Paper

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Joseph R. H. Manning, Lev Sarkisov
Schematic of data pipeline developed in this study, using text mining to extract structured data about published ZIF-8 synthesis protocols, and thereby build information models about the synthesis process.
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Digital Discovery, 2023, Advance Article
DOI: 10.1039/D3DD00063J, Paper

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  This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Elena Zamaraeva, Christopher M. Collins, Dmytro Antypov, Vladimir V. Gusev, Rahul Savani, Matthew S. Dyer, George R. Darling, Igor Potapov, Matthew J. Rosseinsky, Paul G. Spirakis
Reinforcement learning accelerates crystal structure prediction by learning a dynamic policy to maximise the reward for exploring new crystal structures.
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