AIMC Journal:
Journal of chemical information and modeling

Showing 481 to 490 of 945 articles

Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods.

Journal of chemical information and modeling Sep 29, 2022
We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for red...
Machine Learning Protein Binding Proteins Kinetics Ligands
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Identifying SNARE Proteins Using an Alignment-Free Method Based on Multiscan Convolutional Neural Network and PSSM Profiles.

Journal of chemical information and modeling Sep 27, 2022
: SNARE proteins play a vital role in membrane fusion and cellular physiology and pathological processes. Many potential therapeutics for mental diseases or even cancer based on SNAREs are also developed. Therefore, there is a dire need to predict th...
Neural Networks, Computer Position-Specific Scoring Matrices SNARE Proteins
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CLADE 2.0: Evolution-Driven Cluster Learning-Assisted Directed Evolution.

Journal of chemical information and modeling Sep 26, 2022
Directed evolution, a revolutionary biotechnology in protein engineering, optimizes protein fitness by searching an astronomical mutational space via expensive experiments. The cluster learning-assisted directed evolution (CLADE) efficiently explores...
Machine Learning Cluster Analysis Proteins Protein Engineering
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DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features.

Journal of chemical information and modeling Sep 26, 2022
Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estim...
Neural Networks, Computer Machine Learning Algorithms Molecular Docking Simulation Protein Binding Membrane Proteins Ligands High-Throughput Screening Assays
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Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials.

Journal of chemical information and modeling Sep 16, 2022
Computational programs accelerate the chemical discovery processes but often need proper three-dimensional molecular information as part of the input. Getting optimal molecular structures is challenging because it requires enumerating and optimizing ...
Neural Networks, Computer Algorithms Molecular Structure Isomerism
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Roughness of Molecular Property Landscapes and Its Impact on Modellability.

Journal of chemical information and modeling Sep 16, 2022
In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property l...
Machine Learning Drug Design
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EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Sep 15, 2022
Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive pe...
Machine Learning Algorithms Protein Binding Proteins Ligands
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Are Deep Learning Structural Models Sufficiently Accurate for Free-Energy Calculations? Application of FEP+ to AlphaFold2-Predicted Structures.

Journal of chemical information and modeling Sep 13, 2022
The availability of AlphaFold2 has led to great excitement in the scientific community─particularly among drug hunters─due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein str...
Prospective Studies Deep Learning Protein Binding Proteins Molecular Dynamics Simulation Ligands Thermodynamics Models, Structural
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A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data.

Journal of chemical information and modeling Sep 13, 2022
Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consumin...
Neural Networks, Computer Microscopy, Atomic Force Calibration Static Electricity Iodides
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Nucleophilicity Prediction Using Graph Neural Networks.

Journal of chemical information and modeling Sep 12, 2022
The quantitative description between chemical reaction rates and nucleophilicity parameters plays a crucial role in organic chemistry. In this regard, the formula proposed by Mayr et al. and the constructed reactivity database are important represent...
Neural Networks, Computer Machine Learning Algorithms Solvents
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