ArticleJournal of chemical theory and computation2024
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Article
- Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- Article
- Sparse Linear Surrogates Match Neural Network Potentials on the SPICE Biomolecular Benchmark with Three Orders of Magnitude Smaller Training Sets.The journal of physical chemistry letters · 2026Article
- Chemical intuition on bond-dissociation energies as an emergent ability of universal machine-learning interatomic potentials.Nature communications · 2026Article
- Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations.Journal of chemical theory and computation · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- A conformational benchmark for optical property prediction with solvent-aware graph neural networks.Communications chemistry · 2026Article
- Repeatability of Relative Free Energy Calculations in Solution with ANI-2x and MACE-OFF23.Journal of chemical theory and computation · 2026Article
- Architecture-Independent Absolute Solvation Free Energy Calculations with Neural Network Potentials.The journal of physical chemistry letters · 2025Article
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- Transferable Neural Network Potentials and Condensed Phase Properties.Journal of chemical information and modeling · 2025Article
- The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions.Scientific data · 2025Article
- Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution.Journal of the American Chemical Society · 2025Article
- Scaling Graph Neural Networks to Large Proteins.Journal of chemical theory and computation · 2025Article
- Fine-tuning molecular mechanics force fields to experimental free energy measurements.bioRxiv : the preprint server for biology · 2025Article
- Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules.Research (Washington, D.C.) · 2025Article
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4 authors.
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Abstract
We describe version 2 of the SPICE data set, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original data set by adding much more sampling of chemical space and more data on noncovalent interactions. We train a set of potential energy functions called Nutmeg on it. They are based on the TensorNet architecture. They use a novel mechanism to improve performance on charged and polar molecules, injecting precomputed partial charges into the model to provide a reference for the large-scale charge distribution. Evaluation of the new models shows that they do an excellent job of reproducing energy differences between conformations even on highly charged molecules or ones that are significantly larger than the molecules in the training set. They also produce stable molecular dynamics trajectories and are fast enough to be useful for routine simulation of small molecules.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.