AIMC Topic: Molecular Structure

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Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans.

ALTEX Jul 2, 2019
Microcystins (MC) represent a family of cyclic peptides with approx. 250 congeners presumed harmful to human health due to their ability to inhibit ser/thr-proteinphosphatases (PPP), albeit all hazard and risk assessments (RA) are based on data of on...
Microcystins Phosphoprotein Phosphatases Animal Use Alternatives Nuclear Proteins Molecular Structure Models, Biological Machine Learning Computer Simulation Humans
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AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.

Journal of chemical information and modeling Jul 1, 2019
Although algebraic graph theory-based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular properties, confirming the common belief that "one cannot ...
Machine Learning Computational Biology Proteins Algorithms Databases, Protein Molecular Docking Simulation Molecular Structure Drug Design
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Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.

Journal of chemical information and modeling Jun 28, 2019
Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, th...
Animals Male Drug Discovery Rats Algorithms Computer Simulation Reproducibility of Results Models, Biological Gene Expression Regulation Chemical and Drug Induced Liver Injury Vinblastine Rats, Sprague-Dawley Structure-Activity Relationship Molecular Structure Machine Learning
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Machine Learning Models Combined with Virtual Screening and Molecular Docking to Predict Human Topoisomerase I Inhibitors.

Molecules (Basel, Switzerland) Jun 4, 2019
In this work, random forest (RF), support vector machine, k-nearest neighbor and C4.5 decision tree, were used to establish classification models for predicting whether an unknown molecule is an inhibitor of human topoisomerase I (Top1) protein. All ...
Molecular Structure Molecular Dynamics Simulation Quantitative Structure-Activity Relationship Topoisomerase I Inhibitors Computer Simulation Reproducibility of Results Humans Molecular Docking Simulation Machine Learning ROC Curve Algorithms
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Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures.

Amino acids May 16, 2019
Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be obtained from known protein structures. The pairwise potential between ato...
Knowledge Bases Models, Molecular Hydrophobic and Hydrophilic Interactions Molecular Structure Protein Conformation Amino Acids Amino Acid Sequence Entropy Proteins
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Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning.

ChemMedChem May 16, 2019
A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targ...
Alzheimer Disease Ligands Acetylcholinesterase Stereoisomerism Cholinesterase Inhibitors Galantamine Biological Products Molecular Structure Drug Evaluation, Preclinical Drug Design Machine Learning Cell Line, Tumor Molecular Docking Simulation Neuroprotective Agents Humans
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Predicting drug-target interaction network using deep learning model.

Computational biology and chemistry Mar 25, 2019
BACKGROUND: Traditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find new ways for drug repurposing, many computational approaches have been proposed to predict drug-target...
Databases, Protein Drug Repositioning Molecular Structure Deep Learning Models, Chemical Protein Binding Genes, Neoplasm Computational Biology Breast Neoplasms Protein Domains Support Vector Machine Antineoplastic Agents Amino Acid Sequence Proteins Databases, Chemical
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GuacaMol: Benchmarking Models for de Novo Molecular Design.

Journal of chemical information and modeling Mar 19, 2019
De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural net...
Isomerism Quantitative Structure-Activity Relationship Monte Carlo Method Molecular Structure Pharmaceutical Preparations Benchmarking Drug Design Deep Learning Models, Molecular
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In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening.

Journal of chemical information and modeling Mar 5, 2019
Reports of successful applications of machine learning (ML) methods in structure-based virtual screening (SBVS) are increasing. ML methods such as convolutional neural networks show promising results and often outperform traditional methods such as e...
Machine Learning Databases, Factual Retrospective Studies Molecular Structure Bias Molecular Docking Simulation Structure-Activity Relationship Benchmarking Ligands
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Shape-Based Generative Modeling for de Novo Drug Design.

Journal of chemical information and modeling Feb 28, 2019
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image ana...
Molecular Conformation Molecular Structure Machine Learning Quantitative Structure-Activity Relationship Pharmaceutical Preparations Drug Design Hydrogen Bonding Hydrophobic and Hydrophilic Interactions Models, Molecular
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