✨ #DigitalDiscovery is proud to announce that we are sponsoring a poster prize at RSC CICAG Chemical Structure Representations 2026 on 8 April at Burlington House, London!

👉 Click the following link if you are interested in attending this meeting! registrations.hg3conferences.co.uk/hg3/360/regi...

Digital Discovery  Home-Data-driven approaches to scientific discoveries<br/><br/>Editor-in-Chief: Al&amp;#225;n Aspuru-Guzik<br/>Impact factor: 5.6<br/>Time to first decision (peer reviewed only):45 days<br/>A gold open access journal. To learn about the publication fees please visit the journal website. Data-driven approaches to scientific discoveries<br/><br/>Editor-in-Chief: Al&amp;#225;n Aspuru-Guzik<br/>Impact factor: 5.6<br/>Time to first decision (peer reviewed only):45 days<br/>A gold open access journal. To learn about the publication fees please visit the journal website.

📢 #DigitalDiscovery Issue 3 is out now and #OpenAccess!
Find the latest research in data-driven approaches to scientific discoveries, including molecular hypergraphs, a survey of sleep-promoting volatiles, high-entropy alloy discovery and much more👇
Get the full issue here:

Americans Shift Beverage Choices Toward Health and Digital Discovery, Survey Finds More than half of U.S. consumers prioritize wellness and use online tools to select drinks, with Gen Z leading the trend

FYI: Americans Shift Beverage Choices Toward Health and Digital Discovery, Survey Finds #HealthBeverages #Wellness #GenZTrends #DigitalDiscovery #BeverageChoices

Americans Shift Beverage Choices Toward Health and Digital Discovery, Survey Finds More than half of U.S. consumers prioritize wellness and use online tools to select drinks, with Gen Z leading the trend

Americans Shift Beverage Choices Toward Health and Digital Discovery, Survey Finds #Wellness #HealthyChoices #DigitalDiscovery #GenZTrends #BeverageIndustry

Digital materials ecosystem: from databases to AI agents for autonomous discovery

New reviews | Chemical Science
東北大学 Hao Li先生らによる総説：データ駆動材料開発の為のデジタルエコシステム🤖⚛️ #AI #digitalDiscovery #autonomousLab

Digital materials ecosystem: from databases to AI agents for autonomous discovery pubs.rsc.org/en/content/a...

For International Women’s Day 2026, we are highlighting some of the impactful work published by excellent women in accelerated science, through a collection of recent #DigitalDiscovery articles:

pubs.rsc.org/en/jour...

Please contact us if you have recent publications you would like to add.

FiberForge: enabling high-throughput simulations of the mechanical properties of helical fibrils The mechanical properties of amyloid-based materials are governed by fibril geometry, sequence, and polymorphism, yet systematic exploration of this vast design space has been limited by the lack of high-throughput modeling tools. Here we present FiberForge, an open-source workflow that automates constructio

📢 Zhongyue (John) Yang introduces FiberForge: an open source workflow that automates molecular modelling, design and characterisation of amyloid fibrils in new #DigitalDiscovery manuscript!

🖥️ Read more here: doi.org/10.1039/D5DD...

#EmergingInvestigators2025

Data augmentation in a triple transformer loop retrosynthesis model Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits their usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate

✨ A new approach in #DigitalDiscovery is providing better ways to utilise data from open-source publicly available datasets!

🔥 Read about how data augmentation can enrich reactivities in the USPTO chemical reaction dataset here: doi.org/10.1039/D5DD...

#OpenSource #CAPS

Our new Digital Discovery themed collection in collaboration with Accelerate Conference 2023–24 has now been published online!

This new themed collection represents a collaboration between #DigitalDiscovery and the Acceleration Consortium.

Read the collection here: rsc.li/DDAccel24!

Deep learning-enabled discovery of low-melting-point ionic liquids Ionic liquids (ILs) are salts that are liquids at ambient conditions (typically below 373 K) and are known for their many unique properties, including low volatility and high thermal stability. Despite the promise of ILs, their targeted design is challenging for several reasons, including (i) the vast number of can

☀️ Don’t miss today’s #DigitalDiscovery read!

➡️ Kim Jelfs and her team report on their new deep-learning enabled workflow for discovering novel low-melting point ionic liquids!

✨ Read more about how their approach here:

ChemBERTa-3: an open source training framework for chemical foundation models The rapid advancement of machine learning in computational chemistry has opened new doors for designing molecules, predicting molecular properties, and discovering novel materials. However, building scalable and robust models for molecular machine learning remains a significant challenge due to the vast size and co

🔥 The newest hot article in #DigitalDiscovery introduces ChemBERTa-3!

🖥️ This open-source training and benchmark framework is designed to train and fine-tune chemical foundation LLMs

👉 Read more about integrating open source LLMs with scientific workflows here!

LivePyxel: accelerating image annotations with a Python-integrated webcam live streaming The lack of flexible annotation tools has hindered the deployment of AI models in some scientific areas. Most existing image annotation software requires users to upload a precollected dataset, which limits support for on-demand pipelines and introduces unnecessary steps to acquire images. This constraint is partic

🖥️ What if image analysis and annotation was seamlessly and automatically?

🔓 LivePyxel is a new open-source tool from researchers at McMaster University for accelerated image analysis!

➡️ Read more and access the software here: doi.org/10.1039/D5DD...

#DigitalDiscovery #OpenSource

Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction Machine learning (ML) approaches have drastically advanced the exploration of structure–property and property–property relationships in computer-aided drug discovery. A central challenge in this field is the identification of molecular descriptors that can effectively capture both geometric- and electronic structur

☀️ Don't miss this #DigitalDiscovery read!

✨ Leonardo Sandonas et al. introduce QUED, a hybrid QM/ML framework that integrates molecular structure and electronic information to deliver accurate predictions of physicochemical and biological properties.

➡️

AI Search Optimization Reference Guide for Digital Visibility

AI has rewritten the rules of #DigitalDiscovery. Instead of links, #AI assistants deliver direct answers — meaning if your brand isn’t cited, you’re invisible. The solution? Master the Engine #Optimization Trifecta: buff.ly/LWYfAnD

#SEO #AEO #GEO #DigitalVisibility

👉 Don’t miss this Advance Article from #DigitalDiscovery featuring Megalodon 🦈, a scalable transformer-based architecture for multi-modal molecular diffusion and flow matching towards de novo 3D-molecule generation!

🖥️ Read more here! doi.org/10.1039/D5DD...

#MolecularDesign

A multi-task learning approach for prediction of missing bioactivity values of compounds for the SLC transporter superfamily Solute carrier (SLC) transporters constitute the largest family of membrane transport proteins in humans. They facilitate the movement of ions, neurotransmitters, nutrients, and drugs. Given their critical role in regulating cellular physiology, they are important therapeutic targets for neurological and psychologi

📢 Today's #DigitalDiscovery read presents a multitask deep neural network model to predict the bioactivity of solute carrier transport target compounds!

🦠 Read more about how their approach is giving way an enlarged information pool of drug candidates here:

✨ #DigitalDiscovery is proud to be sponsoring a prize at this years RSC Analyticode happening in London on March 16!

📢 Don't miss your chance to submit an abstract by tomorrow (January 30) or to register for the conference (March 7)!

👉 registrations.hg3conferences.co.uk/hg3/frontend...

📢 The first issue of #DigitalDiscovery for 2026 is now online and #OpenAccess! 🔓

⬇️In this issue read about ML-driven materials discovery, quantum computing, benchmarking self-driving labs, reaction prediction, FAIR-compliant workflows and much more!

✨ Read it here! pubs.rsc.org/en/journals/...

AI-driven robotic crystal explorer for rapid polymorph identification Crystallisation is central to purification and to determining structure and material properties, yet small changes in conditions can produce many different polymorphs with distinct behaviours. Because crystallisation depends on multiple variables including solvent, temperature, pressure, and atmosphere and often pr

✨ In their newest #DigitalDiscovery article, Edward Lee and Daniel Salley introduce their robotic crystal search engine!

💎 Their new tool combines robotic automation, computer vision and AI making way for the autonomous discovery of new crystal polymorphs!

🖥️ Read more:

CAMLC

📢 #DigitalDiscovery is proud to be sponsoring a poster prize at the third edition of the CAMLC workshop in Zaragoza, Spain on June 2-5 this year!

🗓️ Don't miss out! Applications are open now and close on February 4th.

👉 See here for more information: camlcworkshop.github...

#CAMLC26

Hierarchical attention graph learning with LLM enhancement for molecular solubility prediction Solubility quantifies the concentration of a molecule that can dissolve in a given solvent. Accurate prediction of solubility is essential for optimizing drug efficacy, improving chemical and separation processes, and waste management, among many other industrial and research applications. Predicting solubility fro

✨ In a new #DigitalDiscovery advance article, Ping Yang and her team are improving the accuracy of solubility predictions with their new neural network-based model, HASolGNN!

📢 Read more HASolGNN here:

Deep learning methods for 2D material electronic properties This review explores the impact of deep learning (DL) techniques on understanding and predicting electronic structures in two-dimensional (2D) materials. We highlight unique computational challenges posed by 2D materials and discuss how DL approaches – such as physics-aware models, generative AI, and inverse design

🖥️ From The University of Manchester, Artem Mishchenko and his team deliver the latest review in #DigitalDiscovery on how deep learning is transforming 2D material structure modelling!

✨ Read more about how AI is accelerating materials discovery here:

One step retrosynthesis of drugs from commercially available chemical building blocks and conceivable coupling reactions In this report, the pharmaceuticals listed in DrugBank were structurally mapped to a commercial catalog of chemical feedstocks through reaction agnostic one step retrosynthetic decomposition. Enumerative combinatorics was utilized to retrosynthesize target molecules into commercially available building blocks, wher

🧪 What if high-throughput computation could guide our hunt of chemical reactivities and synthetic strategies?

✨ New #DigitalDiscovery release features using enumerative combinatorics to map pathways between targets molecules and commercial catalogues!

👉 Read more here

PEMD: a high-throughput simulation and analysis framework for solid polymer electrolytes Solid polymer electrolytes exhibit limitations in room-temperature ionic conductivity and electrochemical stability. While molecular simulations and electronic-structure theory are able to sample these key properties at the molecular scale, the field currently lacks integrated, automated tools for end-to-end assess

✨ Tingzheng Hou and his team are working to bridge atomistic modeling with data-driven materials discovery in their new #DigitalDiscovery manuscript where they introduce their open-source python package, polymer electrolyte modeling and discovery (PEMD)!

👉 Read it here!

Toward accelerating rare-earth metal extraction using equivariant neural networks The separation of rare-earth metals, vital for numerous advanced technologies, is hampered by their similar chemical properties, making ligand discovery a significant challenge. Traditional experimental and quantum chemistry approaches for identifying effective ligands are often resource-intensive. We introduce a m

New neural network methods from Ankur Gupta and Wibe A. de Jong provide a rapid and cost-effective way to accelerate the discovery of new ligands for rare-earth element extraction!

👉 Read the full article here: doi.org/10.1039/D5DD...

#DigitalDiscovery #MachineLearning #NeuralNetworks

The logo for the conference linked in the post

#DigitalDiscovery and @pccp.rsc.org are proud to be sponsoring prizes at 2026's Chemical Compound Space Conference held in Munich from March 10th to 13th. Don't miss your chance to participate. Registration closes on the 15th of January!

Find out more here: ccsc2026.github.io/

Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography The integration of automated synthesis and machine learning (ML) is transforming analytical chemistry by enabling data-driven approaches to method development. Chromatographic column selection, a critical yet time-consuming step in separation science, stands to benefit substantially from such advances. Here,

🖥️ Yuuya Nagata and his team combine automated synthesis and machine learning models in their new #DigitalDiscovery manuscript which works to transform chromatographic workflows through a new model relating retention time to molecular substructure!

👉 Read more here:

Our final #DigitalDiscovery newsletter of the year is now live on the blog!
Catch up on new article types, award winners, research highlights, Editorial Board developments, and upcoming themed collections.
Read the full update: blogs.rsc.org/dd/2025/12/0...

🚨 Digital Discovery Issue 10 is out now!
Includes a new article by Nobel laureate Omar Yaghi et al.:
"Comparison of LLMs in extracting synthesis conditions and generating Q&A datasets for MOFs" 🧠

🔗 Read the issue: pubs.rsc.org/en/jour...

#DigitalDiscovery #OmarYaghi #LLMs #MOFs #Research #AI

🚨 New article out! Discover how AI-driven molecular representation learning is transforming drug discovery and materials design.
From 3D models to hybrid learning, explore the our latest article 👉doi.org/10.1039/D5DD...
#DigitalDiscovery #AI #Research