Research infographic – Cell morphology-guided de novo hit design by conditioning GANs on phenotypic image features

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:

An infographic summarising the research in the linked article

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

Research infographic – A fully automated platform for photoinitiated RAFT polymerization

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.

An infographic summarising the research in the linked paper

Read the full article, open access, here:

A fully automated platform for photoinitiated RAFT polymerization

Research infographic – Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions

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:

An infographic describing the contents of the linked paper

Find out more in the full article below!

“Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions”

Research infographic – Operator-independent high-throughput polymerization screening based on automated inline NMR and online SEC

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.

An infographic describing the linked article

Read the full open access article here:

Operator-independent high-throughput polymerization screening based on automated inline NMR and online SEC

Digital Discovery, 2022, 1, 519–526, DOI: 10.1039/D2DD00035K

Professor Cesar de la Fuente joins the team as an Associate Editor

Welcome to Digital Discovery!

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's picture

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

AI and drug discovery

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.

Digital Discovery cover imagePlease join us in welcoming Professor Cesar de la Fuente to Digital Discovery!

Research infographic – Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

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.

An infographic describing the linked article

Read the full open access article here:

Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

Shizhao Lu, Brian Montz, Todd Emrick and Arthi Jayaraman, Digital Discovery, 2022, 1, 816–833, DOI: 10.1039/D2DD00066K

Research infographic – Plot2Spectra: an automatic spectra extraction tool

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:

A research infographic describing the linked paper

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

Introducing our new advisory board

We are delighted to introduce our Advisory Board for Digital Discovery!

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.

 

Research infographic – “Data mining crystallization kinetics”

Our newest research infographic shares work by Brown et al. from Digital Discovery issue 5, on a crystallisation classification system and its corresponding database:

An infogrpahic describing the research in the linked paper

Read the full open-access article here:

Data mining crystallization kinetics

Research infographic – “Machine learning enabling high-throughput and remote operations at large-scale user facilities”

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:

A research infographic describing the linked paper

Read the full open-access article here:

“Machine learning enabling high-throughput and remote operations at large-scale user facilities”