ArticleJournal of chemical theory and computation2025
PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method.
Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Intrinsic Reactivity of the Elusive Cyclic N,N-Diacyliminium Ions in Mannich Reactions: The Complete Reactivity Order for Non, N-Mono and N,N-Diacyliminium Ions.The Journal of organic chemistry · 2026Article
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- Benchmarking Nanoscale Noncovalent Complexes at the Two-Hundred-Atom Scale with Converged Local CCSD(T).The journal of physical chemistry. A · 2026Article
- Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Quantum-Driven High-Throughput Docking: Advances in Molecular Scoring and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications.Journal of chemical information and modeling · 2025Article
- The Enduring Relevance of Semiempirical Quantum Mechanics.The journal of physical chemistry. A · 2025Review
- AIQM2: organic reaction simulations beyond DFT.Chemical science · 2025Article
- Benchmark of Approximate Quantum Chemical and Machine Learning Potentials for Biochemical Proton Transfer Reactions.Journal of chemical theory and computation · 2025Article
- Quantitative Structure-Activity Relationships for Human Galectin-3 Inhibitors: Insights from Quantum Chemical Interaction Energy Terms.Journal of chemical information and modeling · 2025Article
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 2025Review
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a Δ-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a Δ-ML method that synergizes the semiempirical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML potential applied as a universal correction. The method demonstrates superior performance over standalone SQM and ML approaches and covers a broader chemical space than its predecessors. It is scalable to systems with thousands of atoms, which makes it applicable to large biomolecular systems. Extensive benchmarking confirms PM6-ML's accuracy and robustness. Its practical application is facilitated by a direct interface to MOPAC. The code and parameters are available at https://github.com/Honza-R/mopac-ml.
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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.