AIMC Journal:
Journal of chemical information and modeling

Showing 471 to 480 of 945 articles

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network.

Journal of chemical information and modeling Nov 7, 2022
The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and ma...
Neural Networks, Computer Machine Learning Protein Binding Proteins Binding Sites
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Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search.

Journal of chemical information and modeling Nov 5, 2022
Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies criteria for various objectives, such as selectivity for a tar...
Artificial Intelligence Drug Design Monte Carlo Method Tyrosine
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Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks.

Journal of chemical information and modeling Oct 31, 2022
In this study, a framework for the prediction of thermophysical properties based on transfer learning from existing estimation models is explored. The predictive capabilities of conventional group-contribution methods and traditional machine-learning...
Neural Networks, Computer Machine Learning Deep Learning Molecular Structure
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ReLMole: Molecular Representation Learning Based on Two-Level Graph Similarities.

Journal of chemical information and modeling Oct 27, 2022
Molecular representation is a critical part of various prediction tasks for physicochemical properties of molecules and drug design. As graph notations are common in expressing the structural information of chemical compounds, graph neural networks (...
Neural Networks, Computer Learning Drug Interactions
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Assessment of the Generalization Abilities of Machine-Learning Scoring Functions for Structure-Based Virtual Screening.

Journal of chemical information and modeling Oct 21, 2022
In structure-based virtual screening (SBVS), it is critical that scoring functions capture protein-ligand atomic interactions. By focusing on the local domains of ligand binding pockets, a standardized pocket Pfam-based clustering (Pfam-cluster) appr...
Machine Learning Protein Binding Cluster Analysis Proteins Ligands
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Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach.

Journal of chemical information and modeling Oct 14, 2022
For many experimentally measured chemical properties that cannot be directly computed from first-principles, the existing physics-based models do not extrapolate well to out-of-sample molecules, and experimental datasets themselves are too small for ...
Neural Networks, Computer Machine Learning
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Fast Prediction of Lipophilicity of Organofluorine Molecules: Deep Learning-Derived Polarity Characters and Experimental Tests.

Journal of chemical information and modeling Oct 12, 2022
Fast and accurate estimation of lipophilicity for organofluorine molecules is in great demand for accelerating drug and materials discovery. A lipophilicity data set of organofluorine molecules (OFL data set), containing 1907 samples, is constructed ...
Deep Learning Water Solubility Solvents 1-Octanol
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Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models.

Journal of chemical information and modeling Oct 11, 2022
Machine learning provides effective computational tools for exploring the chemical space via deep generative models. Here, we propose a new reinforcement learning scheme to fine-tune graph-based deep generative models for molecular design tasks. We ...
Machine Learning Drug Design
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Functional Output Regression for Machine Learning in Materials Science.

Journal of chemical information and modeling Oct 10, 2022
In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a functi...
Machine Learning Models, Statistical Polymers Materials Science
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Large-Scale Distributed Training of Transformers for Chemical Fingerprinting.

Journal of chemical information and modeling Oct 4, 2022
Transformer models have become a popular choice for various machine learning tasks due to their often outstanding performance. Recently, transformers have been used in chemistry for classifying reactions, reaction prediction, physiochemical property ...
Machine Learning
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