From the 1 of 5 linked papers with an AI index.
5 papers
HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration
Daria A. Ryabchenko, Pavel Gurevich, Shamil Kadyrov +5
The paper introduces HEDGEHOG, a six‑stage filtration benchmark that mimics industrial hit‑identification workflows to rigorously evaluate generative molecular models for drug disc…
Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants
Dmitry Korogod, Alexander V. Shapeev, Ivan S. Novikov
We present two models with explicit long-range electrostatics in the form of Coulomb interactions. Both models include point charges depending on their local atomic environments, a…
Automated Prediction of Thermodynamic Properties via Bayesian Free-Energy Reconstruction from Molecular Dynamics
Ekaterina Spirande, Timofei Miryashkin, Andrei Kolmakov +1
Accurate free-energy calculations are essential for predicting thermodynamic properties and phase stability, but existing methods are limited: phonon-based approaches neglect anhar…
Incorporating Coulomb interactions with fixed charges in Moment Tensor Potentials and Equivariant Tensor Network Potentials
Dmitry Korogod, Olga Chalykh, Max Hodapp +3
In this work, we incorporate long-range electrostatic interactions in the form of the Coulomb model with fixed charges into the functional form of short-range machine-learning inte…
Moment Tensor Potential and Equivariant Tensor Network Potential with explicit dispersion interactions
Olga Chalykh, Dmitry Korogod, Ivan S. Novikov +3
In this study, we investigate the effect of incorporating explicit dispersion interactions in the functional form of machine learning interatomic potentials (MLIPs), particularly i…