How do we understand and predict molecular structures?
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Understanding and Predicting Molecular Structures
Introduction to Molecular Structure Prediction
Understanding and predicting molecular structures is a cornerstone of chemistry and molecular biology. The ability to accurately determine and predict the structures of molecules is essential for interpreting their chemical and physical properties, which in turn influences fields ranging from drug design to materials science.
Computational Methods in Molecular Structure Prediction
High-Resolution Modeling of Protein Structures
Recent advancements in computational modeling have significantly improved our ability to predict and design macromolecular structures and interactions. These high-resolution predictions are crucial for interpreting genome sequences and creating macromolecules with novel functions, thereby contributing to both biology and medicine1.
Machine Learning Approaches
Machine learning has emerged as a powerful tool for molecular property prediction. Two prominent methods include neural networks applied to molecular fingerprints and graph convolutional neural networks (GCNNs). GCNNs, in particular, have shown promise in constructing learned molecular representations by operating on the graph structure of molecules, often outperforming traditional models in both public and proprietary datasets2 5.
Quantum Chemistry and Spectroscopy
Quantum chemistry combined with spectroscopic techniques provides another robust approach for accurate molecular structure determination. These methods allow for precise predictions of molecular geometries, which are essential for understanding thermochemistry, kinetics, and spectroscopic signatures. Joint theory-spectroscopy strategies, such as the semiexperimental approach, leverage both experimental data and quantum-chemical computations to achieve high accuracy6.
Theoretical Foundations and Topological Approaches
Topological Theory of Molecular Structure
A topological theory of molecular structure defines atoms and bonds in terms of the observable distribution of charge within a molecular system. This theory links to quantum mechanics, providing a predictive framework for structural stability and offering a rigorous definition of molecular structure4.
Electronic Structure Theory for Molecular Crystals
Electronic structure methods, including periodic density functional theory and fragment-based approaches, are crucial for modeling molecular crystals and polymorphism. These methods help predict various crystal properties, such as lattice energies and phase diagrams, which are vital for understanding polymorphic behavior in materials9.
Practical Applications and Industry Relevance
Predicting Organic Crystal Structures
Predicting the crystalline structures of organic compounds is a significant challenge with substantial industrial relevance. Methods like global lattice energy minimization are commonly used, although they still face limitations. Reliable predictions can aid in designing materials with novel properties and avoiding undesirable changes during industrial development8.
Rapid Prediction Using Deep Learning
Deep learning techniques, such as point cloud-based strategies, enable rapid prediction of liquid structures from single molecular configurations. These methods can quickly provide insights into the structural properties of liquids, reducing the computational cost associated with traditional molecular dynamics simulations10.
Conclusion
The field of molecular structure prediction is rapidly evolving, with significant contributions from computational modeling, machine learning, quantum chemistry, and topological theories. These advancements are enhancing our ability to predict and understand molecular structures, thereby driving progress in various scientific and industrial applications. As these methods continue to improve, they will play an increasingly critical role in the development of new materials and the interpretation of complex biological systems.
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Most relevant research papers on this topic
Progress in Modeling of Protein Structures and Interactions
Recent progress in modeling protein structures and interactions suggests that high-resolution prediction and design will significantly contribute to biology and medicine.
Highly CitedAnalyzing Learned Molecular Representations for Property Prediction
Our graph convolutional model consistently outperforms existing models in molecular property prediction, offering significant improvements over existing industrial workflows.
Rigorous JournalHighly CitedAccurate molecular structures of small- and medium-sized molecules
Advanced computational methodologies enable accurate molecular structure determinations for small and medium-sized molecules, crucial for understanding their chemical and physical properties.
A topological theory of molecular structure
This theory demonstrates that atoms and bonds can be defined and stabilized using topological properties of the observable distribution of charge, linking molecular structure to quantum mechanics.
Highly CitedConvolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
Convolutional neural networks can effectively predict physical properties of molecules by identifying important features of atom clusters.
Highly CitedDiving for Accurate Structures in the Ocean of Molecular Systems with the Help of Spectroscopy and Quantum Chemistry.
A joint theoretical-experimental approach can accurately determine molecular structures, addressing challenges in understanding thermochemistry, kinetics, and spectroscopic signatures of molecular systems.
Highly CitedMachine learning based energy-free structure predictions of molecules, transition states, and solids
Graph-To-Structure (G2S) effectively predicts atomistic structures of molecules, transition states, and solids without energy optimization, improving accuracy and efficiency in physics, chemistry, materials, and biology.
Current approaches to predicting molecular organic crystal structures
Current methods for predicting molecular organic crystal structures show progress and limitations, aiding in materials design and avoiding undesirable changes in form.
Highly CitedModeling Polymorphic Molecular Crystals with Electronic Structure Theory.
Electronic structure methods are crucial for modeling molecular crystals and predicting their properties, including polymorphism, for various applications in pharmaceuticals, organic semiconductor materials, and foods.
Highly CitedRapid Prediction of a Liquid Structure from a Single Molecular Configuration Using Deep Learning
Deep learning can rapidly predict the structural properties of liquids from a single molecular configuration, providing rapid insight into liquid structure under various pressure and temperature conditions.
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