ReviewNature reviews. Chemistry2020
Exploring chemical compound space with quantum-based machine learning.
Review in Nature reviews. Chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
112 citing papers in PubMed.
- Hierarchical generative modeling for the design of multi-component systems.Journal of cheminformatics · 2026Article
- Artificial intelligence-driven magnetic property prediction and materials discovery for next-generation spintronics.RSC advances · 2026Review
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Bridging the accuracy-speed divide in reactive molecular dynamics with QuantaMind MD.Science advances · 2026Article
- Data Compression of the D1200 Suite: Achieving the Optimal Balance Between Size and Accuracy with the Diet-D200 Subset.Journal of computational chemistry · 2026Article
- Exploring the chemical space of transition-metal cluster thermodynamics via automated first-principles calculations and machine learning.Nature communications · 2026Article
- Integrating Coupled-Cluster Theory Within AI Workflows for Accurate Molecular Geometries.Journal of computational chemistry · 2026Article
- Flow matching for reaction pathway generation.Nature communications · 2026Article
- EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties.Journal of cheminformatics · 2026Article
- Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning.Bioinformatics (Oxford, England) · 2026Article
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- The (r)evolution of chemical space and molecular modeling: a time-resolved perspective.Journal of computer-aided molecular design · 2026Review
- A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era.Journal of chemical theory and computation · 2026Review
- Transitive prediction of small-molecule function through alignment of high-content screening resources.Nature biotechnology · 2026Article
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Predicting electronic properties of molecules: a stacking ensemble model for HOMO and LUMO energy estimation.RSC advances · 2026Article
- Synergizing Chemical and AI Communities for Advancing Laboratories of the Future.ACS central science · 2026Review
- Adaptive Transition-State Refinement with Learned Equilibrium Flows.Journal of chemical information and modeling · 2026Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space - the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.
Identifiers
37127950What OpenQuestion holds
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.