Digital Discovery, 2023, 2,1347-1353
DOI: 10.1039/D3DD00090G, Paper

Open Access

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

Yuki Murakami, Shoichi Ishida, Yosuke Demizu, Kei Terayama
MODAN is a multi-objective Bayesian framework for automated design of antimicrobial peptides containing various non-proteinogenic amino acids and side-chain stapling.
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Digital Discovery, 2023, 2,1297-1310
DOI: 10.1039/D3DD00005B, Paper

Open Access

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Michael Moran, Michael W. Gaultois, Vladimir V. Gusev, Matthew J. Rosseinsky
Site-Net is a transformer architecture that models the periodic crystal structures of inorganic materials as a labelled point set of atoms and relies entirely on global self-attention and geometric information to guide learning.
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Digital Discovery, 2023, 2,1326-1333
DOI: 10.1039/D3DD00108C, Paper

Open Access

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Przemyslaw Frei, Philip J. Kitson, Alexander X. Jones, Leroy Cronin
A new modular approach to 3D printed reactionware design is presented, and its effectiveness demonstrated in the synthesis of a number of structurally related, diazirine based, photoaffinity probes.
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Digital Discovery, 2023, 2,1452-1460
DOI: 10.1039/D3DD00088E, Paper

Open Access

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David E. Graff, Edward O. Pyzer-Knapp, Kirk E. Jordan, Eugene I. Shakhnovich, Connor W. Coley
Pretrained molecular representations are often thought to provide smooth, navigable latent spaces; analysis by ROGI-XD suggests they are no smoother than fixed descriptor/fingerprint representations.
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Digital Discovery, 2023, 2,1233-1250
DOI: 10.1039/D3DD00113J, Perspective

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Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti Badhwar, Joshua D. Bocarsly, Andres M. Bran, Stefan Bringuier, L. Catherine Brinson, Kamal Choudhary, Defne Circi, Sam Cox, Wibe A. de Jong, Matthew L. Evans, Nicolas Gastellu, Jerome Genzling, María Victoria Gil, Ankur K. Gupta, Zhi Hong, Alishba Imran, Sabine Kruschwitz, Anne Labarre, Jakub Lála, Tao Liu, Steven Ma, Sauradeep Majumdar, Garrett W. Merz, Nicolas Moitessier, Elias Moubarak, Beatriz Mouriño, Brenden Pelkie, Michael Pieler, Mayk Caldas Ramos, Bojana Ranković, Samuel G. Rodriques, Jacob N. Sanders, Philippe Schwaller, Marcus Schwarting, Jiale Shi, Berend Smit, Ben E. Smith, Joren Van Herck, Christoph Völker, Logan Ward, Sean Warren, Benjamin Weiser, Sylvester Zhang, Xiaoqi Zhang, Ghezal Ahmad Zia, Aristana Scourtas, K. J. Schmidt, Ian Foster, Andrew D. White, Ben Blaiszik
We report the findings of a hackathon focused on exploring the diverse applications of large language models in molecular and materials science.
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Digital Discovery, 2023, 2,1380-1389
DOI: 10.1039/D3DD00076A, Paper

Open Access

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Elvin Lo, Pin-Yu Chen
We benchmark zeroth-order methods for molecule optimization and discuss how they may be effectively used for practical molecule discovery.
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Digital Discovery, 2023, 2,1390-1403
DOI: 10.1039/D3DD00064H, Paper

Open Access

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Jonathan P. Mailoa, Xin Li, Jiezhong Qiu, Shengyu Zhang
We use a mixture of incomplete-labelled molecule property databases to conditionally generate new molecules with multiple property co-constraints.
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Digital Discovery, 2023, 2,1404-1413
DOI: 10.1039/D3DD00114H, Paper

Open Access

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Weiyi Gong, Tao Sun, Hexin Bai, Shah Tanvir ur Rahman Chowdhury, Peng Chu, Anoj Aryal, Jie Yu, Haibin Ling, John P. Perdew, Qimin Yan
We demonstrate that contrastive representation learning is a computationally efficient and flexible method to incorporate physical constraints, especially those defined by equalities, in machine-learning-based density functional design.
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Digital Discovery, 2023, 2,1354-1367
DOI: 10.1039/D3DD00060E, Paper

Open Access

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

Daniel R. Wong, David J. Logan, Santosh Hariharan, Robert Stanton, Djork-Arné Clevert, Andrew Kiruluta
Fluorescent-based microscopy screens carry a broad range of phenotypic information about how compounds affect cellular biology.
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Digital Discovery, 2023, 2,1414-1424
DOI: 10.1039/D3DD00071K, Paper

Open Access

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Henrik Schopmans, Patrick Reiser, Pascal Friederich
We used synthetically generated crystals to train ResNet-like models to enhance the prediction of space groups from ICSD powder X-ray diffractograms. The results show improved generalization to unseen structure types compared to previous approaches.
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