Top Tweets for #ML4Molecules
🚀 New preprint: EquiHGNN 🧬
Rotationally equivariant hypergraph neural networks for molecular modeling!
🔹 Combines symmetry + topology
🔹 Scales to QM9, OPV, PCQM4Mv2
📄 https://t.co/3dynJbewtP
💻 https://t.co/PiO3pF06mP
#AI4Science #GDL #ML4Molecules
🚨The only thing better than polycyclic aromatic systems is *MORE* polycyclic aromatic systems!
🥳Now in @ScientificData: COMPAS-2 -- our new dataset of 500k (yes, half a million!) PASs, made by @eddMayo and @Sabyasa13824679.
https://t.co/diHQnUHjKw
#ML4Molecules #Chemtwitter
Didn't catch MolE at #ML4Molecules? @omendezlucio will be presenting MolE, a powerful molecular foundation model for property prediction, today at 2pm ET at #LMRL2022 @lmrl_bio.
Join us for our #LMRL2022 spotlight talk today at 2pm ET to learn about MolE, a molecular foundation model we created to predict chemical properties. @lmrl_bio
For a full list of our research presented at #NeurIPS2022 workshops, visit our website: https://t.co/scxV9NK7yY
ICYMI: Today we presented our recently-released preprint about MolE, a molecular foundation model we created to predict chemical properties, at #ML4Molecules (#NeurIPS2022).
Read the paper to learn how this is accelerating #drugdiscovery at Recursion: https://t.co/GYCKbPLbIk
Thanksgiving update. #ML4Molecules workshop paper @NeurIPSConf By @john_kevin_cava @JhnVant @Ankita1Shukla @abhisekhsingha1 Nicholas Ho, and Ross Maciejewski. “Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics". Happy thanksgiving !!
Don’t forget to submit your contributions to the ELLIS “Machine learning for molecule discovery” workshop: https://t.co/IMd6sPw0Jl
Less than 1 week to the deadline (Nov 7th) #chemtwitter #ML4Molecules #ArtificialIntelligence #compchem
a lot of #MachineLearning & #ML4Molecules work is based on the idea that molecular structure encoded in eg SMILES is the essence of chemistry.
just FYI for CS people, graphs are to molecules what Turing machines are to computers
a convenient and highly idealized abstraction
Molecules cannot be ‘discovered’ by machine learning with graphs. Molecules can be described as graphs but they are NOT graphs. If you want to ‘discover’ a molecule I suggest you get into the lab...
Really enjoyed #ML4Molecules today! Thanks to the organizers and everyone who presented. All the papers are here: https://t.co/5uTUNxnaYi
Gonna do what everyone else is doing and share our poster for #ML4Molecules @NeurIPS2020
Joint work with @oooctavian and @cwcoley
Excited to present our work on guided drug discovery and prediction using evidential uncertainty! @NeurIPS2020 #ML4Molecules
Spotlight talk 👉 https://t.co/msoRKbs0Op
Poster 31 @ 4pm EST today 👉 https://t.co/BTyiqgGQcc
Drop by with questions or to discuss!
Check out my poster with @rbhar90 at today's #NeurIPS2020 #ML4Molecules workshop! @NeurIPSConf @deep_chem
This year’s #ML4Molecules workshop at #NeurIPS2020 started with an excellent talk and Q&A by Nadine (@foo_fighterin)! Let's digitalise drug discovery.
-> Workshop link: https://t.co/kk8OMHm7dT
Check out our contributed talk on FlowMO: A Gaussian Process Library for Molecular Property Prediction today at 14:50 GMT at the #ML4Molecules Workshop @NeurIPSConf with @Henrymossmoss
https://t.co/0A7klsdCJw
Tutorials on string and Tanimoto kernels:
https://t.co/C5Q0FycCY4
Join us tomorrow for the #ML4Molecules workshop at @NeurIPSConf to learn more about OrbNet! (contributed talk 2-3) Multi-task for better accuracy; analytical gradients for accurate geometry optimizations.
@ZhuoranQ @AnimaAnandkumar @EntosAI
OrbNet 🕷️ gradients are available, yielding excellent accuracy for geometry optimizations with 1000-fold reduction in DFT cost.
https://t.co/WWaoU7ugtz
Accepted to the #ML4Molecules workshop at @NeurIPSConf
Great collaboration with @ZhuoranQ @AnimaAnandkumar and @EntosAI
Our rotationally covariant actor-critic for 3D molecular design is out!
Come to our poster on Dec 12 #ML4Molecules @NeurIPSConf to learn how our RL agent exploits the symmetries of the design process.
https://t.co/ThQ9zjEnA4
w/ @rpinsler, Gábor Csányi and @jmhernandez233
.@grumpybavarian will present at the #NeurIPS2020 #ML4Molecules on MolBERT: a BERT-based model architecture that learns molecular representations which achieve SOTA results on drug discovery benchmark tasks.
Paper: https://t.co/XKxgUsvK3N
Code: https://t.co/XGAaqeWKQj
In our latest work at the #ML4molecules workshop at #NeurIPS2020, we propose
- DimeNet++, roughly as fast as SchNet, but 4x more accurate
- the COLL dataset, which goes far beyond equilibrium mols (such as QM9)
- early steps in uncertainty for GNNs in MD
https://t.co/1Ft0BRweaC
Check out our work accepted to the #ML4Molecules workshop at @NeurIPSConf 🥳🎉 We develop GNN aggregation functions for molecules with tetrahedral chirality--a first attempt at tackling chirality with 2D graph networks
Paper: https://t.co/ubCEWf4835
Code: https://t.co/OIK8Ud8rla
I'm excited for the upcoming #ML4Molecules Workshop at @NeurIPSConf 2020 and to share some of our work in progress in the @keiser_lab for learning on conformational ensembles with graph neural networks: https://t.co/hyRSpdEyIx
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