How do we understand and predict molecular structures?
Searched over 200M research papers
Research Analysis by Consensus
Consensus Meter
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 medicine.
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 datasets .
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 accuracy.
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 structure.
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 materials.
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 development.
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 simulations.
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.
Sources and full results
Most relevant research papers on this topic
Progress in Modeling of Protein Structures and Interactions
Analyzing Learned Molecular Representations for Property Prediction
Accurate molecular structures of small- and medium-sized molecules
A topological theory of molecular structure
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
Diving for Accurate Structures in the Ocean of Molecular Systems with the Help of Spectroscopy and Quantum Chemistry.
Machine learning based energy-free structure predictions of molecules, transition states, and solids
Current approaches to predicting molecular organic crystal structures
Modeling Polymorphic Molecular Crystals with Electronic Structure Theory.
Rapid Prediction of a Liquid Structure from a Single Molecular Configuration Using Deep Learning
Try another search
How does increasing Se-content affect Cd 50 S 50−x Se x system thin films?
What are the health effects of urban green spaces and how can I take advantage of them?
Sustainability business models
Is soy healthy?
The potential of energy harvesting technologies in powering IoT devices and wearable technology.
social sustainability