Image de couverture de livre

The impact of conformer quality on learned representations of molecular conformer ensembles

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Adams, Keir, Coley, Connor W.
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Chemical Physics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics
par: Viswanath, Siddharth, et autres
Publié: (2024)

Generative Artificial Intelligence for Navigating Synthesizable Chemical Space
par: Gao, Wenhao, et autres
Publié: (2024)

REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring
par: Kim, Taewon, et autres
Publié: (2024)

Challenging reaction prediction models to generalize to novel chemistry
par: Bradshaw, John, et autres
Publié: (2025)

Revealing the Relationship Between Publication Bias and Chemical Reactivity with Contrastive Learning
par: Gao, Wenhao, et autres
Publié: (2024)

Physics-informed generative model for drug-like molecule conformers
par: Williams, David C., et autres
Publié: (2024)

Procedural Synthesis of Synthesizable Molecules
par: Sun, Michael, et autres
Publié: (2024)

Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation
par: Wan, Yue, et autres
Publié: (2023)

Motional representation; the ability to predict odor characters using molecular vibrations
par: Harada, Yuki, et autres
Publié: (2025)

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning
par: Grambow, Colin A., et autres
Publié: (2023)

Deconstructing equivariant representations in molecular systems
par: Lee, Kin Long Kelvin, et autres
Publié: (2024)

Enhancing molecular dynamics with equivariant machine-learned densities
par: Bogojeski, Mihail, et autres
Publié: (2026)

Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics
par: Pengmei, Zihan, et autres
Publié: (2024)

Integer linear programming for unsupervised training set selection in molecular machine learning
par: Haeberle, Matthieu, et autres
Publié: (2024)

Understanding active learning of molecular docking and its applications
par: Kim, Jeonghyeon, et autres
Publié: (2024)

Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks
par: Ramos, Mayk Caldas, et autres
Publié: (2023)

Efficient molecular conformation generation with quantum-inspired algorithm
par: Li, Yunting, et autres
Publié: (2024)

E(3)-equivariant models cannot learn chirality: Field-based molecular generation
par: Dumitrescu, Alexandru, et autres
Publié: (2024)

Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why
par: Xie, Pinchen, et autres
Publié: (2024)

Kernel Ridge Regression for conformer ensembles made easy with Structured Orthogonal Random Features
par: Karandashev, Konstantin
Publié: (2025)

Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplings
par: Schultz, Jonathan D., et autres
Publié: (2025)

Collisional alignment and molecular rotation control chemi-ionization of individual conformers
par: Ploenes, L., et autres
Publié: (2024)

Tabular foundation models for in-context prediction of molecular properties
par: Hicham, Karim K. Ben, et autres
Publié: (2026)

FlashMD: long-stride, universal prediction of molecular dynamics
par: Bigi, Filippo, et autres
Publié: (2025)

Multitask methods for predicting molecular properties from heterogeneous data
par: Fisher, Katharine, et autres
Publié: (2024)

Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning
par: Hu, Frank, et autres
Publié: (2024)

Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors
par: Mao, Yun-Wen, et autres
Publié: (2023)

Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents
par: Williams, Nicholas J., et autres
Publié: (2024)

Vib2Conf: AI-driven discrimination of molecular conformations from vibrational spectra
par: Lu, Xin-Yu, et autres
Publié: (2026)

Leap: molecular synthesisability scoring with intermediates
par: Calvi, Antonia, et autres
Publié: (2024)

ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
par: Adams, Keir, et autres
Publié: (2024)

Probing the effects of broken symmetries in machine learning
par: Langer, Marcel F., et autres
Publié: (2024)

Machine learning for accuracy in density functional approximations
par: Voss, Johannes
Publié: (2023)

Knowing when to trust machine-learned interatomic potentials
par: Mehdi, Shams, et autres
Publié: (2026)

Pushing the limits of unconstrained machine-learned interatomic potentials
par: Bigi, Filippo, et autres
Publié: (2026)

3DReact: Geometric deep learning for chemical reactions
par: van Gerwen, Puck, et autres
Publié: (2023)

Representing spherical tensors with scalar-based machine-learning models
par: Domina, Michelangelo, et autres
Publié: (2025)

Extending machine learning model for implicit solvation to free energy calculations
par: Dey, Rishabh, et autres
Publié: (2025)

Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
par: Falk, John, et autres
Publié: (2023)

Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs
par: Neporozhnii, Ihor, et autres
Publié: (2026)