We’re pleased to share this new infographic on work from Marin Zapata et al., showing that phenotypic features of cell images can guide a GAN to drug candidates:
Read the full open access article here:
Cell morphology-guided de novo hit design by conditioning GANs on phenotypic image features
Paula A. Marin Zapata, Oscar Méndez-Lucio, Tuan Le, Carsten Jörn Beese, Jörg Wichard, David Rouquié and Djork-Arné Clevert, Digital Discovery, 2023, 2, 91–102, DOI: 10.1039/D2DD00081D
The first infographic from Digital Discovery volume 2 highlights a paper from Gormley et al. on a fully automated system for synthesising new polymers. The automated lab includes independent control of each reaction in a well plate using a custom light box.
Read the full article, open access, here:
A fully automated platform for photoinitiated RAFT polymerization
Machine-learning prediction of enantioselectivity of C-H bond activation reactions is the focus of this infographic, on an exciting new paper by Hoque and Sunoj:
Find out more in the full article below!
Our newest research infographic shares research by Junkers et al., demonstrating the use of automation to reduce operator-to-operator inconsistencies in high-throughput RAFT screening while improving efficiency.
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Digital Discovery, 2022, 1, 519–526, DOI: 10.1039/D2DD00035K
Nanomaterial research often involves characterising structures through microscopy which could be accelerated by automation. This new infographic describes a new workflow by Jayaraman et al. for analysing nanowire morphologies.
Read the full open access article here:
Shizhao Lu, Brian Montz, Todd Emrick and Arthi Jayaraman, Digital Discovery, 2022, 1, 816–833, DOI: 10.1039/D2DD00066K
We’re pleased to share a new research infographic describing Plot2Spectra, a tool for digitising published spectroscopy data, by Jiang, Chan et al. in issue 5:
Read the full open access article here:
Plot2Spectra: an automatic spectra extraction tool
Weixin Jiang, Kai Li, Trevor Spreadbury, Eric Schwenker, Oliver Cossairt and Maria K. Y. Chan, Digital Discovery, 2022, 1, 719–731. DOI: 10.1039/D1DD00036E
Our newest research infographic shares work by Brown et al. from Digital Discovery issue 5, on a crystallisation classification system and its corresponding database:
Read the full open-access article here:
Digital Discovery issue 4 features work by Daniel Olds, et al. on machine learning approaches designed for non-ML-expert light source users. Find out more in the infographic below:
Read the full open-access article here:
“Machine learning enabling high-throughput and remote operations at large-scale user facilities”
Digital Discovery issue 3 features work by Teresa Head-Gordon, et al. on NewtonNet, summarised in this new infographic!
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Digital Discovery issue 3 features work by Ryo Tamura, Koji Tsuda, et al. on SLEPA, summarised in this new infographic!
Read the full article here:
“Self-learning entropic population annealing for interpretable materials design”
Jiawen Li, Jinzhe Zhang, Ryo Tamura and Koji Tsuda, Digital Discovery, 2022, 1, 295–302, DOI: 10.1039/D1DD00043H
