AIMC Journal:
Journal of chemical information and modeling

Showing 541 to 550 of 958 articles

Analysis of Training and Seed Bias in Small Molecules Generated with a Conditional Graph-Based Variational Autoencoder─Insights for Practical AI-Driven Molecule Generation.

Journal of chemical information and modeling Feb 7, 2022
The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions remain concerning how factors like training, data set, and seed bias impact the technology's utility t...
Artificial Intelligence Models, Molecular Receptors, Dopamine D2
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De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update.

Journal of chemical information and modeling Feb 7, 2022
Nowadays, machine learning and deep learning approaches are widely utilized for generative chemistry and computer-aided drug design and discovery such as de novo peptide and protein design, where target-specific peptide-based/protein-based therapeuti...
Neural Networks, Computer Artificial Intelligence Machine Learning Peptides Proteins
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TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.

Journal of chemical information and modeling Feb 4, 2022
Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM...
Neural Networks, Computer Molecular Dynamics Simulation Quantum Theory Ligands Thermodynamics
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Yuel: Improving the Generalizability of Structure-Free Compound-Protein Interaction Prediction.

Journal of chemical information and modeling Feb 1, 2022
Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening and drug target identification. Deep learning-based approaches have recently been adapted to predict binding affinities and th...
Drug Delivery Systems Deep Learning Proteins
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Interpretation of Structure-Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence.

Journal of chemical information and modeling Jan 26, 2022
In silico models based on Deep Neural Networks (DNNs) are promising for predicting activities and properties of new molecules. Unfortunately, their inherent black-box character hinders our understanding, as to which structural features are important ...
Neural Networks, Computer Artificial Intelligence Computer Simulation Drug Design Structure-Activity Relationship
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Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy.

Journal of chemical information and modeling Jan 19, 2022
We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed gr...
Neural Networks, Computer Machine Learning Solvents Solutions Thermodynamics Entropy
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Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity.

Journal of chemical information and modeling Jan 12, 2022
There is a lack of scalable quantitative measures of reactivity that cover the full range of functional groups in organic chemistry, ranging from highly unreactive C-C bonds to highly reactive naked ions. Measuring reactivity experimentally is costly...
Neural Networks, Computer Machine Learning Physics
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Benchmarking Molecular Feature Attribution Methods with Activity Cliffs.

Journal of chemical information and modeling Jan 12, 2022
Feature attribution techniques are popular choices within the explainable artificial intelligence toolbox, as they can help elucidate which parts of the provided inputs used by an underlying supervised-learning method are considered relevant for a sp...
Neural Networks, Computer Artificial Intelligence Machine Learning Algorithms
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PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.

Journal of chemical information and modeling Jan 3, 2022
Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented K, a convolutional neural network that predicted the ...
Neural Networks, Computer Proteins Ligands
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DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks.

Journal of chemical information and modeling Dec 27, 2021
A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting crit...
Neural Networks, Computer Databases, Factual Reproducibility of Results Cluster Analysis Databases, Chemical
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