AIMC Topic: Molecular Structure

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Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Molecular pharmaceutics Feb 6, 2023
Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to...
Machine Learning Algorithms Drug Discovery Molecular Structure Pharmaceutical Preparations Pharmacokinetics Quantitative Structure-Activity Relationship
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Persistent Path-Spectral (PPS) Based Machine Learning for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Jan 16, 2023
Molecular descriptors are essential to quantitative structure activity/property relationship (QSAR/QSPR) models and machine learning models. Here we propose persistent path-spectral (PPS), PPS-based molecular descriptors, and PPS-based machine learni...
Machine Learning Protein Binding Proteins Molecular Structure Ligands
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Traditional Machine and Deep Learning for Predicting Toxicity Endpoints.

Molecules (Basel, Switzerland) Dec 26, 2022
Molecular structure property modeling is an increasingly important tool for predicting compounds with desired properties due to the expensive and resource-intensive nature and the problem of toxicity-related attrition in late phases during drug disco...
Machine Learning Drug Discovery Deep Learning Molecular Structure
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Machine-Learning-Guided Discovery of Electrochemical Reactions.

Journal of the American Chemical Society Dec 2, 2022
The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all dis...
Machine Learning Molecular Structure Chemistry, Organic
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Exploration of Chemical Space Guided by PixelCNN for Fragment-Based De Novo Drug Discovery.

Journal of chemical information and modeling Dec 1, 2022
We report a novel framework for achieving fragment-based molecular design using pixel convolutional neural network (PixelCNN) combined with the simplified molecular input line entry system (SMILES) as molecular representation. While a widely used rec...
Neural Networks, Computer Drug Design Drug Discovery Molecular Structure
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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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New opportunities in integrative structural modeling.

Current opinion in structural biology Oct 21, 2022
Integrative structural modeling enables structure determination of macromolecules and their complexes by integrating data from multiple sources. It has been successfully used to characterize macromolecular structures when a single structural biology ...
Artificial Intelligence Molecular Structure Macromolecular Substances Cryoelectron Microscopy Electron Microscope Tomography
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Training recurrent neural networks as generative neural networks for molecular structures: how does it impact drug discovery?

Expert opinion on drug discovery Oct 17, 2022
INTRODUCTION: Deep learning approaches have become popular in recent years in de novo drug design. Generative models for molecule generation and optimization have shown promising results. Molecules trained on different chemical data could regenerate ...
Humans Neural Networks, Computer Drug Design Drug Discovery Molecular Structure
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Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration.

International journal of molecular sciences Sep 24, 2022
In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemica...
Machine Learning Molecular Structure Protein Kinase Inhibitors Space Flight src-Family Kinases
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Orbital Mixer: Using Atomic Orbital Features for Basis-Dependent Prediction of Molecular Wavefunctions.

Journal of chemical theory and computation Sep 19, 2022
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous studies focuses on generating predictions for only a f...
Neural Networks, Computer Molecular Structure Quantum Theory
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