AIMC Topic: Molecular Conformation

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Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.

Journal of chemical information and modeling Oct 31, 2019
Structure-based drug design is critically dependent on accuracy of molecular docking scoring functions, and there is of significant interest to advance scoring functions with machine learning approaches. In this work, by judiciously expanding the tra...
Drug Design Water Protein Binding Machine Learning Molecular Docking Simulation Ligands Molecular Conformation Small Molecule Libraries Proteins Databases, Protein Humans
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From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Molecular pharmaceutics Sep 10, 2019
Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generat...
Humans Ligands Molecular Conformation Drug Discovery Small Molecule Libraries Neural Networks, Computer Proteins Quantitative Structure-Activity Relationship Drug Design Models, Chemical
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Enhanced Deep-Learning Prediction of Molecular Properties via Augmentation of Bond Topology.

ChemMedChem Aug 23, 2019
Deep learning has made great strides in tackling chemical problems, but still lacks full-fledged representations for three-dimensional (3D) molecular structures for its inner working. For example, the molecular graph, commonly used in chemistry and r...
Ligands Datasets as Topic Molecular Conformation Deep Learning Models, Molecular
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Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships.

Journal of computer-aided molecular design Aug 21, 2019
In this work, computational compound screening strategies on the basis of two- and three-dimensional (2D and 3D) molecular representations were investigated including similarity searching and support vector machine (SVM) ranking. Calculations based o...
Protein Binding Support Vector Machine Molecular Conformation Computational Chemistry Structure-Activity Relationship Machine Learning User-Computer Interface
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A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

Journal of chemical information and modeling Aug 8, 2019
Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize ...
Models, Molecular Molecular Conformation Neural Networks, Computer Density Functional Theory
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Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method.

International journal of molecular sciences Jul 22, 2019
Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain popul...
Polypharmacology Quantitative Structure-Activity Relationship Databases, Pharmaceutical Humans Machine Learning Drug Discovery HIV Infections Algorithms Bayes Theorem Protein Binding Anti-HIV Agents Hepacivirus Models, Molecular HIV-1 Antiviral Agents Hepatitis C Molecular Structure Molecular Conformation
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Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines.

Journal of chemical information and modeling May 15, 2019
A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and...
Automation Molecular Conformation Acetaldehyde Support Vector Machine Models, Molecular
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Prediction of Hemolytic Toxicity for Saponins by Machine-Learning Methods.

Chemical research in toxicology Apr 9, 2019
Saponins are a type of compounds bearing a hydrophobic steroid/triterpenoid moiety and hydrophilic carbohydrate branches. The majority of the saponins demonstrate a broad range of prominent pharmacological activities. Nevertheless, many saponins also...
Molecular Conformation Saponins Hemolysis Humans Hydrophobic and Hydrophilic Interactions Machine Learning Erythrocytes
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Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks.

Journal of chemical information and modeling Apr 8, 2019
Several recent reports have shown that long short-term memory generative neural networks (LSTM) of the type used for grammar learning efficiently learn to write Simplified Molecular Input Line Entry System (SMILES) of druglike compounds when trained ...
Molecular Conformation Models, Molecular Neural Networks, Computer Cheminformatics Pharmaceutical Preparations
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Machine Learning Prediction of H Adsorption Energies on Ag Alloys.

Journal of chemical information and modeling Apr 2, 2019
Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, ...
Machine Learning Adsorption Models, Molecular Hydrogen Silver Alloys Molecular Conformation Thermodynamics
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