ArticleScientific data2023
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
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Who cites it
102 citing papers in PubMed.
- Hierarchical generative modeling for the design of multi-component systems.Journal of cheminformatics · 2026Article
- Using structure to evaluate the accuracy and transferability of machine learning potentials of biomolecules.RSC advances · 2026Article
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials.Chemical science · 2026Article
- Accurate and Time-Efficient Condensed-Phase Free Energy Simulations with Reaction Specific Δ-Machine Learning Potentials in CHARMM.Journal of chemical theory and computation · 2026Article
- Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential.The journal of physical chemistry letters · 2026Article
- Article
- Lithium Surface Restructuring and Dendrite-Like Protrusion Formation in Li-S Electrolytes From Reactive Molecular Simulations With Machine-Learned Potentials.Journal of computational chemistry · 2026Article
- Bias in Universal Machine-Learned Interatomic Potentials and Its Effects on Fine-Tuning.Journal of chemical theory and computation · 2026Article
- Chemical intuition on bond-dissociation energies as an emergent ability of universal machine-learning interatomic potentials.Nature communications · 2026Article
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations.Journal of chemical theory and computation · 2026Article
- Generalization of long-range machine learning potentials in complex chemical spaces.Digital discovery · 2026Article
- NN-xTB: density functional accuracy at semi empirical speed with neural network extended tight binding.Nature communications · 2026Article
- Developing and Benchmarking Sage 2.3.0 with the AshGC Neural Network Charge Model.Journal of chemical theory and computation · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials.Journal of chemical information and modeling · 2026Article
42 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for training potentials relevant to simulating drug-like small molecules interacting with proteins. It contains over 1.1 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 15 elements, charged and uncharged molecules, and a wide range of covalent and non-covalent interactions. It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, along with other useful quantities such as multipole moments and bond orders. We train a set of machine learning potentials on it and demonstrate that they can achieve chemical accuracy across a broad region of chemical space. It can serve as a valuable resource for the creation of transferable, ready to use potential functions for use in molecular simulations.
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Registered trials
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.