Here we go 🤓
#compchem

Announcing CACHE challenge #6: predict 100 ligands for the immuno-oncology target SETDB1. We will test them experimentally and release all data publicly. bit.ly/3QBgYoK #compchem Structural Genomics Consortium (SGC) Conscience

Analyzing the Active Site and Predicting the Overall Activity of Alloy Catalysts #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

9 participants to CACHE3 had their predicted SARS-CoV2 NSP3 hit(s) experimentally confirmed. They selected up to 50 analogs to generate SAR. We just received the 296 compounds from Enamine. Screening about to begin. bit.ly/3vLxyKN
Structural Genomics Consortium (SGC) Conscience #compchem

Was glad to chat today with David Shaw 🤓
#compchem

#HighlightOfTheWeek Correlation between the two dihedral angles of a phthalate molecule.
#compchem
pubs.acs.org/doi/10.1021/ac…

It was a pleasure to present our work on the antimicrobial peptide at International Membrane Biophysics Meeting in Drübeck! 🧑‍🔬🧑‍💻
Böckmann Lab Christian Nehls
#compchem #membranes #biophysics

Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

[2405.01834] 3-center and 4-center 2-particle Gaussian AO integrals on modern accelerated processors #compchem arxiv.org/abs/2405.01834

Transferability of Machine Learning Models for Geogenic Contaminated Groundwaters #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

[2405.00279] Beyond a Richardson-Gaudin mean-field: Slater-Condon rules and perturbation theory #compchem arxiv.org/abs/2405.00279

#compchem Good read

Latest paper from our group. Nice collaboration between Yazdan Maghsoud Arkanil Roy and Emmett to investigate the Effects of the Y432S Cancer-Associated Variant on the Reaction Mechanism of Human DNA Polymerase κ
JCIM & JCTC Journals #compchem #compbiophys
pubs.acs.org/doi/full/10.10…

Webinar:From the Femtoscale to the Mesoscale and Back: An Integrated Multiscale Approach
bit.ly/4btF3Wf
#compchem #materialsdesign #materialsproperties #VASP #molecularmodeling #atomisticsimulation #molecularsimulation #MLPG #LAMMPS #materialsdesign #energyindustry

#compchem Latest group preprint:
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials.
arxiv.org/abs/2405.01491

A new GPU-accelerated #opensource library for building, training and running force-field-enhanced neural network…

Predicting equilibrium distributions for molecular systems with deep learning #machinelearning #compchem nature.com/articles/s4225…

#compchem Good read: Perspective: Atomistic simulations of water and aqueous systems with machine learn ing potentials pubs.aip.org/aip/jcp/articl…

Great new work by Daniele Soccodato et al Synopsys QuantumATK ISMN CNR Università di Roma Tor Vergata -'Machine learned environment dependent corrections for a spds empirical tight-binding basis' - iopscience.iop.org/article/10.108… #machinelearning #electronicstructure #condmat #compchem #materials #AI #atomistic

Thrilled to share our recent PNASNews publication. Liquid–liquid transition and ice crystallization in a machine-learned coarse-grained water model | PNAS #compchem #Water #MachineLearning
Utah Chemistry Rajat Kumar Department of Chemistry IITG pnas.org/doi/10.1073/pn…