@DanielSWigh profile picture
Daniel Wigh @DanielSWigh
Joined April 2018
  • Tweets 36
  • Following 118
  • Followers 80
1 Photos and videos
I made the @Forbes 30 Under 30 list 🤯
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Daniel Wigh retweeted
Y Combinator
@ycombinator
17 Mar 2025
Congrats to the team @ReactWise on the $3.4M round! ReactWise's copilot for chemical process optimization enables AI-driven robotic laboratories and speeds up pharmaceutical manufacturing R&D. techcrunch.com/2025/03/17/yc…

YC-backed ReactWise is applying AI to speed up drug manufacturing | TechCrunch

Artificial intelligence continues stirring things up in chemistry. To wit: Y Combinator-backed Cambridge, U.K.-based ReactWise is using AI to speed up

techcrunch.com
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Daniel Wigh retweeted
Arda Göreci
@ArdaGoreci
2 Sep 2024
🚀Excited to announce: Open-source AlphaFold3 implementation! 🚀 I am thrilled to announce one of the models we have been building for the last 8-weeks at Ligo - an open-source implementation of DeepMind’s frontier model, AlphaFold3! Here’s what we have learned, a thread (1/11):
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Daniel Wigh retweeted
ReactWise@ReactWise
20 Aug 2024
We are excited to share a video with insights into our ambitions at ReactWise to drive efficiency and innovation in pharma & biotech. Reach out to us via info@reactwise.com youtube.com/watch?v=THda6XYI… Video credit: Nathan Pitt ©University of Cambridge

Introducing ReactWise

ReactWise offers software for identifying ideal (bio)chemical proce...

youtube.com
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ReactWise just launched on @ycombinator's Launch YC! ReactWise - AI co-pilot for wet lab chemistry. Check them out: ycombinator.com/launches/L7A…

Launch YC: ReactWise - AI co-pilot for wet lab chemistry | Y Combinator

No-code chemical process optimization software for pharma and biotech

ycombinator.com
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@ReactWise
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Cleaning chemical reaction data sets has never been easier! Check out ORDerly, our new paper in JCIM, about our open source pipeline for cleaning chemical reaction data sets stored in Open Reaction Database (ORD) doi.org/10.1021/acs.jcim.4c0…

ORDerly: Data Sets and Benchmarks for Chemical Reaction Data

Machine learning has the potential to provide tremendous value to life sciences by providing models that aid in the discovery of new molecules and reduce the time for new products to come to market....

pubs.acs.org
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@marcos_felt @alexanderpomb @LapkinLab @SynTech_CDT @ReactWise @iDMTcambridge @Cambridge_Uni
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We also created three new benchmarks: ORDerly-forward (forward prediction), ORDerly-retro (retrosynthesis), and ORDerly-condition (the first condition prediction benchmark).
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Do you work with boronic acids? If so, check out this web app we built based on a recent paper of ours! protodeboronation-prediction… Simply input the SMILES string of a boronic acid, and it will show you the predicted rate of protodeboronation (boronic acid degradation)!

protodeboronation-prediction.streamlit.app

Visit our interactive website here!

protodeboronation-prediction.streamlit.app
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The rate prediction is made as a function of pH in 50/50 water/dioxane. If our dataset does not contain your queried molecule, it will find the most similar one to show instead (by tanimoto similarity).
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You can find our algorithm explained in detail in our paper: doi.org/10.1021/acs.jpca.2c0… #chemistry #protodeboronation #computationalchemistry #boronicacid #crosscoupling

Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density...

Computational reaction prediction has become a ubiquitous task in chemistry due to the potential value accurate predictions can bring to chemists. Boronic acids are widely used in industry; however,...

pubs.acs.org
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Excited to share our work on predicting the rate of the protodeboronation reaction! To anyone working with boronic acids, you can find predicted rates of protodeboronation for 50 academically and industrially relevant molecules in the SI of our paper! pubs.acs.org/doi/10.1021/acs…

Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density...

Computational reaction prediction has become a ubiquitous task in chemistry due to the potential value accurate predictions can bring to chemists. Boronic acids are widely used in industry; however,...

pubs.acs.org
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Special thanks to my supervisors Prof. Lapkin and Prof. Goodman, and our collaborators at UCB Pharma.
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In this work we predict protodeboronation rates by considering each mechanistic pathway separately, and calculating characteristic energy differences using Density Functional Theory (DFT), without the need for further experimentation.
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Protodeboronation is a significant degradation pathway for boronic acids, though understanding how quickly it occurs (and how to avoid it) remains a challenge, and relies on expert knowledge and wet-lab experiments.
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Daniel Wigh retweeted
Lapkin Lab@LapkinLab
15 Mar 2023
Our new paper creates a workflow for predicting protodeboronation rates from a SMILES string! Amazing work from @DanielSWigh! #chemtwitter pubs.acs.org/doi/10.1021/acs…
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My paper ‘A review of molecular representation in the age of machine learning’, has just been published! it explores how to describe molecules in a way that is understandable to computers and compatible with machine learning workflows. Check it out here: wires.onlinelibrary.wiley.co…

A review of molecular representation in the age of machine learning

Understanding how to best represent molecules in a machine-readable format is a key challenge.

wires.onlinelibrary.wiley.com
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Might be interesting if you want to learn more about string (e.g. InChI/SMILES), feature based (e.g. fingerprints) and computer learned (e.g. VAEs) representations.
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Thank you to my supervisors @AlexeiLapkin and @goodman_j for all your excellent support and feedback, and thank you @SynTech_CDT and @ucb_news for funding this work!
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