Chemistry, Biophysics and Coding
In front of a screen
Joined June 2016
- Tweets465
- Following2.5K
- Followers1.4K
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Mark Waller retweeted
Very exciting to share: AQuaRef- #CryoEM and XRD protein structure refinement with #MachineLearning #QM Teamwork by @MalgoBiczysko @Kavi_Dil @Zubatyuk @adrian_roitberg @waller_lab @hokru_science @PendingAI and @LBNLresearch team 💪
biorxiv.org/content/10.1101/… #compchem
Mark Waller retweeted
Pending AI has been invited to participate in the @AWS and @peakxvpartners Generative AI United States Delegation in Palo Alto, California this week. Our Vice President, Business Development, David Almeida Cardoso, PhD, is in attendance.
Mark Waller retweeted
🚨 Job alert! 🚨
Three (!) postdoctoral positions in quantum computing for chemistry
Total package: $119k – $148k p.a. for up to 4 years
Some details below; apply by 11 March here: tinyurl.com/yck44d43
Thanks to the team at MongoDB for this post: mongodb.com/blog/post/spotli…
Mark Waller retweeted
After 1.5 yeas our review “Integrating #QSAR modelling and deep learning in #drugdiscovery: the emergence of deep QSAR” in @NatRevDrugDisc is out! Great team effort with Alex Tropsha, Alexandre Varnek, Gisbert Schneider, and Art Cherkasov #compchem nature.com/articles/s41573-0… – at Pittsburgh, PA
Mark Waller retweeted
Delighted to share our recent work in which we developed MeGen, an RL model, to efficiently generate low-energy Ga cluster 3D structures. MeGen outperforms the conventional approach in time and resources for generating ground-state (GS) geometries and low-lying isomers.
Great new work by @RohitModee U Deva Priyakumar et al @iiit_hyderabad @csir_ncl @AcSIR_India - 'MeGen - generation of gallium metal clusters using #reinforcementlearning' - bit.ly/42FguRu #machinelearning #materials #condmat #metals #algorithms #DFT #electronicstructure
Mark Waller retweeted
It's back!
AI in Drug Discovery 2022 - A Highly Opinionated Literature Review
practicalcheminformatics.blo…
Mark Waller retweeted
Hey, chemtweeps, @gabepgomes & I have a joint postdoc #chempostdoc opening. The position will focus on developing ML/AI algorithms for autonomous experimentation at @CarnegieMellon Cloud Lab cloudlab.cmu.edu/. Opps to work with @NSF_CCAS! #compchem
apply.interfolio.com/117136
Mark Waller retweeted
The ELLIS ML4Molecules workshop will also happen this year on November 28 in VIRTUAL format!
Please find the announcement and the call for papers here: moleculediscovery.github.io/…
Looking forward to your contributions!
Mark Waller retweeted
A new paper just published in @JCIM_JCTC! Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials. Collaboration with @adrian_roitberg #compchem #MachineLearning pubs.acs.org/doi/full/10.102…
Mark Waller retweeted
I can't believe that our book “Quantum Chemistry in the Age of Machine Learning” is finally out!
I can't express enough my gratitude to everyone involved!
dr-dral.com/book-quantum-che…
Mark Waller retweeted
CDK 2.8 is out! github.com/cdk/cdk/releases/…
Mark Waller retweeted
NMR Spectrum to Structure @iiit_hyderabad @kcisatiiith @IHUB_Data @yashaswi_201 @JPhysChem #compchem #NMR #drugdesign #chem #MachineLearning
. @JPhysChem Deep Reinforcement Learning for Molecular Inverse Problem of NMR Spectra to Molecular Structure, Bhuvanesh Sridharan, Sarvesh Mehta, Yashaswi Pathak, U. Deva Priyakumar* @deva_priyakumar @iiit_hyderabad pubs.acs.org/doi/10.1021/acs… #NMRchat #NMR
Mark Waller retweeted
I am beyond excited to announce that QMugs, our large open-source database of drug-like molecules alongside their computed quantum mechanical properties, is now available on @ScientificData! 🧵[1/5]
(work w. @clemensisert & @atzkenneth)
nature.com/articles/s41597-0…
Mark Waller retweeted
We develop a new #MachineLearning scheme for protein pKa. It is based on representation learning & combines atomic environment vector (AEV) and learned QM representation from ANI-2x neural network potential. Our latest paper in @ChemicalScience pubs.rsc.org/en/content/arti… #compchem
Mark Waller retweeted
A machine-learning model as Mat2Spec based on probabilistic embedding enables to predict materials’ phonon density of states and electronic density of states instead of single scalar properties go.nature.com/3H1KYCp #mlchem#compchem @johnmgregoire @Caltech @BerkeleyLab @Cornell
Mark Waller retweeted
Our ML seminar @EPFL_CHEM_Tweet starts again tomorrow at 15.15 with a talk by @jmhernandez233
memento.epfl.ch/event/machin…
Mark Waller retweeted
Let me introduce to you our new Christmas submission: BuRNN 🔥 A QM/MM approach with neural networks. Wonderful collaboration with @mms_boku @BettinaLier @marquetand and my first manuscript as corresponding author @MoltagDK @FWF_at #compchem #deeplearning
arxiv.org/abs/2112.11395
