AIMC Topic: Molecular Conformation

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An end-to-end deep learning method for protein side-chain packing and inverse folding.

Proceedings of the National Academy of Sciences of the United States of America May 30, 2023
Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to ...
Deep Learning Proteins Amino Acids Molecular Conformation Protein Conformation Protein Folding
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Enhanced sampling in explicit solvent by deep learning module in FSATOOL.

Journal of computational chemistry May 16, 2023
FSATOOL is an integrated molecular simulation and data analysis program. Its old molecular dynamics engine only supports simulations in vacuum or implicit solvent. In this work, we implement the well-known smooth particle mesh Ewald method for simula...
Deep Learning Molecular Dynamics Simulation Solvents Molecular Conformation
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An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules.

Molecules (Basel, Switzerland) Dec 5, 2022
Accurate conformational energetics of molecules are of great significance to understand maby chemical properties. They are also fundamental for high-quality parameterization of force fields. Traditionally, accurate conformational profiles are obtaine...
Neural Networks, Computer Quantum Theory Molecular Conformation Physical Phenomena
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Conformal prediction under feedback covariate shift for biomolecular design.

Proceedings of the National Academy of Sciences of the United States of America Oct 18, 2022
Many applications of machine-learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what data to consider next. For example, a data-driven approach for designi...
Machine Learning Algorithms Feedback Molecular Conformation Uncertainty
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Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions.

Physical chemistry chemical physics : PCCP Oct 5, 2022
Efficient prediction of the partition coefficient (log ) between polar and non-polar phases could shorten the cycle of drug and materials design. In this work, a descriptor, named 〈 - ACSFs〉, is proposed to take the explicit polarization effects in t...
Machine Learning Solvents Carboxylic Acids Molecular Conformation Entropy
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Conformer-RL: A deep reinforcement learning library for conformer generation.

Journal of computational chemistry Aug 24, 2022
Conformer-RL is an open-source Python package for applying deep reinforcement learning (RL) to the task of generating a diverse set of low-energy conformations for a single molecule. The library features a simple interface to train a deep RL conforme...
Neural Networks, Computer Algorithms Polymers Molecular Conformation Reinforcement, Psychology
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Characterizing Metastable States with the Help of Machine Learning.

Journal of chemical theory and computation Aug 3, 2022
Present-day atomistic simulations generate long trajectories of ever more complex systems. Analyzing these data, discovering metastable states, and uncovering their nature are becoming increasingly challenging. In this paper, we first use the variati...
Machine Learning Animals Cattle Proteins Molecular Conformation Aprotinin
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Employing Artificial Neural Networks to Identify Reaction Coordinates and Pathways for Self-Assembly.

The journal of physical chemistry. B Jul 6, 2022
Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales...
Neural Networks, Computer Computer Simulation Molecular Conformation
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TocoDecoy: A New Approach to Design Unbiased Datasets for Training and Benchmarking Machine-Learning Scoring Functions.

Journal of medicinal chemistry Jun 1, 2022
Development of accurate machine-learning-based scoring functions (MLSFs) for structure-based virtual screening against a given target requires a large unbiased dataset with structurally diverse actives and decoys. However, most datasets for the devel...
Machine Learning Ligands Molecular Conformation
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Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.

Journal of biomolecular NMR May 27, 2022
Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been developed to characterise the kinetics and thermodynamics of the exchange processes, as well as the structur...
Humans Neural Networks, Computer Artificial Intelligence Magnetic Resonance Imaging Molecular Conformation Nuclear Magnetic Resonance, Biomolecular Magnetic Resonance Spectroscopy
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