ArticleJournal of chemical theory and computation2023
QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Quantum-Centric Alchemical Free Energy Calculations.Journal of chemical theory and computation · 2026Article
- The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and machine learning potential.Chemical science · 2026Article
- Automated Force Field Developer and Optimizer Platform: Torsion Reparameterization.Journal of chemical information and modeling · 2026Article
- The Enduring Relevance of Semiempirical Quantum Mechanics.The journal of physical chemistry. A · 2025Review
- Recent Developments in Amber Biomolecular Simulations.Journal of chemical information and modeling · 2025Article
- DFT-Based Elucidation and Evaluation of Selenium-Modified Tacrine Derivatives: Theoretical and Physicochemical Insights for Alzheimer's Disease Therapy.Molecules (Basel, Switzerland) · 2025Article
- FE-ToolKit: A Versatile Software Suite for Analysis of High-Dimensional Free Energy Surfaces and Alchemical Free Energy Networks.Journal of chemical information and modeling · 2025Article
- Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications.The journal of physical chemistry. B · 2025Article
- DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials.Journal of chemical theory and computation · 2025Article
- QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials.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
- DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials.Journal of chemical information and modeling · 2025Article
- Fast and Accurate Prediction of Tautomer Ratios in Aqueous Solution via a Siamese Neural Network.Journal of chemical theory and computation · 2025Article
- Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions.Biophysics reviews · 2025Review
- PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method.Journal of chemical theory and computation · 2025Article
- Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.The journal of physical chemistry. B · 2024Article
- Software Infrastructure for Next-Generation QM/MM-ΔMLP Force Fields.The journal of physical chemistry. B · 2024Article
- Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine-Thymine (G-T) Mispairs Using Combined Quantum Mechanical/Molecular Mechanical and Machine Learning Potentials.Molecules (Basel, Switzerland) · 2024Article
- Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.Nature communications · 2024Article
- Training machine learning potentials for reactive systems: A Colab tutorial on basic models.Journal of computational chemistry · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
We report QDπ-v1.0 for modeling the internal energy of drug molecules containing H, C, N, and O atoms. The QDπ model is in the form of a quantum mechanical/machine learning potential correction (QM/Δ-MLP) that uses a fast third-order self-consistent density-functional tight-binding (DFTB3/3OB) model that is corrected to a quantitatively high-level of accuracy through a deep-learning potential (DeepPot-SE). The model has the advantage that it is able to properly treat electrostatic interactions and handle changes in charge/protonation states. The model is trained against reference data computed at the ωB97X/6-31G* level (as in the ANI-1x data set) and compared to several other approximate semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3, MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QDπ model is demonstrated to be accurate for a wide range of intra- and intermolecular interactions (despite its intended use as an internal energy model) and has shown to perform exceptionally well for relative protonation/deprotonation energies and tautomers. An example application to model reactions involved in RNA strand cleavage catalyzed by protein and nucleic acid enzymes illustrates QDπ has average errors less than 0.5 kcal/mol, whereas the other models compared have errors over an order of magnitude greater. Taken together, this makes QDπ highly attractive as a potential force field model for drug discovery.
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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.