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.
Read the full open access article here:
Digital Discovery, 2022, 1, 519–526, DOI: 10.1039/D2DD00035K
We are delighted to welcome Professor Cesar de la Fuente-Nunez from the University of Pennsylvania, USA, as a new Associate Editor for Digital Discovery.
Cesar de la Fuente is a Presidential Assistant Professor at the University of Pennsylvania, where he leads the Machine Biology Group whose goal is to combine the power of machines and biology to help prevent, detect, and treat infectious diseases. Specifically, he pioneered the development of the first antibiotic designed by a computer with efficacy in animals, designed algorithms for antibiotic discovery, reprogrammed venoms into antimicrobials, created novel resistance-proof antimicrobial materials, and invented rapid low-cost diagnostics for COVID-19 and other infections.
De la Fuente is an NIH MIRA investigator and has received recognition and research funding from numerous other groups. Prof. de la Fuente has received over 50 awards. He was recognized by MIT Technology Review as one of the world’s top innovators for “digitizing evolution to make better antibiotics”. He was selected as the inaugural recipient of the Langer Prize, an ACS Kavli Emerging Leader in Chemistry, and received the AIChE’s 35 Under 35 Award and the ACS Infectious Diseases Young Investigator Award.
In 2021, he received the Thermo Fisher Award, and the EMBS Academic Early Career Achievement Award “For the pioneering development of novel antibiotics designed using principles from computation, engineering, and biology.” Most recently, Prof. de la Fuente was awarded the prestigious Princess of Girona Prize for Scientific Research, the ASM Award for Early Career Applied and Biotechnological Research and has been named a Highly Cited Researcher by Clarivate several times.
Professor de la Fuente has given over 200 invited lectures and his scientific discoveries have yielded over 110 publications, including papers in Nature Biomedical Engineering, Nature Communications, PNAS, ACS Nano, Cell, Nature Chemical Biology, Advanced Materials, and multiple patents.
Read some of Cesar’s recent papers below.
Deep generative models for peptide design
Fangping Wan, Daphne Kontogiorgos-Heintz and Cesar de la Fuente-Nunez
Digital Discovery, 2022, 1, 195-208
Morgan Craig, Juan Caicedo, Payel Das, James Collins, Francesca Grisoni, Cesar de la Fuente-Núñez, Yu-Shan Lin, Jian Tang
Cell Reports Physical Science, 2022, 3, 101142
Submit your work to Cesar here.
Please join us in welcoming Professor Cesar de la Fuente to Digital Discovery!
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
The Digital Discovery Advisory Board is made up of outstanding researchers from chemistry, materials science, and biotechnology who contribute to the journal as reviewers and writers, provide strategic feedback, and act as community advocates. Learn more about our entire Editorial and Advisory Boards on our website and get to know our newest Advisory Board members and some of their research samples below
Meet our new Advisory Board members:
Abigail Doyle
University of California, Los Angeles, USA
The Doyle lab conducts research at the interface of organic, organometallic, physical organic, and computational chemistry.
Alexandre Tkatchenko, University of Luxembourg, Luxemburg
Dr Tkatchenko develops first-principles computational models to study a wide range of complex materials.
Berend Smit , EPFL, Switzerland
Professor Smit’s focuses on the application and development of novel molecular simulation techniques.
Cecilia Clementi, Freie Universität Berlin, Germany
Dr Clementi’s research focuses on the development and application of methods for the modelling of complex biophysical processes.
Conor Coley, MIT, USA
Professor Coley develops new methods at the intersection of data science, chemistry, and laboratory automation.
Koji Tsuda, The University of Tokyo, Japan
Dr Tsuda’s research background and current interests involve machine learning, computational biology, and computational materials science.
Marwin Segler, Microsoft, Germany
Dr Segler pioneered modern machine learning for molecular design, and chemical synthesis planning.
Heather Kulik, MIT, USA
Professor Kulik completed postdoctoral training at Lawrence Livermore and Stanford, prior to joining MIT as a faculty member.
Jan Jensen, University of Copenhagen, Denmark
Dr Jensen works in molecular discovery and reactivity prediction in the University of Copenhagen.
Isao Tanaka, Kyoto University, Japan
Professor Isao Tanaka is working on first principles calculations and data-centric science with special interests on issues in materials science and engineering.
Ola Engkvist, AstraZeneca and Chalmers University of Technology, Sweden
Professor Engkvist main research interests are deep learning based molecular de novo design, synthetic route prediction and large scale molecular property predictions.
Silvana Botti, Friedrich Schiller University Jena, Germany
Dr Botti’s research focuses on computational materials design, as well as on the development and application of many-body treatments for theoretical spectroscopy.
Shuye Ping Ong, University of California San Diego, USA
Professor Shuye leads the Materials Virtual Lab at UCSD, focusing on the interdisciplinary application of materials science, computer science, and data science to accelerate materials design.
Pablo Carbonell, University of Valencia, Spain
Dr Carbonell’s research interests are in automated design for metabolic engineering and synthetic biology.
Please join us in welcoming all of our new Advisory Board members to Digital Discovery!
Digital Discovery is open for submissions. Find out more on the journal webpage, sign up for email alerts or submit your manuscript now.
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”
We are pleased to announce free desktop seminar to introduce Digital Discovery and share interesting new work in the journal’s scope. In this 100-minute seminar, meet the authors and editors of Digital Discovery and learn about the exciting experimental and computational work being performed to accelerate scientific progress.
If you’re interested in either seminar but can’t make the date, register your interest and we’ll send you a link to the recording afterwards.
(Update 20 October 2022 – The recording of this webinar is now available to view at this link.)
Wednesday 12 October, 1700 JST / 1600 CST / 1330 IST / 1000 CEST / 0900 BST
| Professor Yuya Oaki
Keio University, Japan Title: “Sparse modeling for small data toward digital discovery.” |
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| Professor Xi Zhu
The Chinese University of Hong Kong, China Title: “Towards the digitalization of chemical experiments.” |
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| Dr Sukriti Singh
University of Cambridge, United Kingdom Title: “Transfer learning for reaction outcome prediction with limited data.” |
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| Prof. Emma Schymanski
University of Luxembourg, Luxembourg Title: “Extraction of chemical structures from literature and patent documents using open access chemistry toolkits: a case study with PFAS.” |
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Digital Discovery issue 3 features work by Teresa Head-Gordon, et al. on NewtonNet, summarised in this new infographic!
Read the full article here:
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
