AIMC Topic: Models, Chemical

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Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

Journal of chemical information and modeling Nov 5, 2019
The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. How...
Density Functional Theory Models, Molecular Gold Neural Networks, Computer Computer Simulation Crystallization Alloys Thermodynamics Titanium Surface Properties Models, Chemical
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Development and Application of a Data-Driven Reaction Classification Model: Comparison of an Electronic Lab Notebook and Medicinal Chemistry Literature.

Journal of chemical information and modeling Sep 26, 2019
Reaction classification has often been considered an important task for many different applications, and has traditionally been accomplished using hand-coded rule-based approaches. However, the availability of large collections of reactions enables d...
Machine Learning Molecular Structure Databases, Chemical Chemistry, Pharmaceutical Models, Chemical
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All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration ICs for 8558 Novartis Assays.

Journal of chemical information and modeling Sep 26, 2019
Profile-quantitative structure-activity relationship (pQSAR) is a massively multitask, two-step machine learning method with unprecedented scope, accuracy, and applicability domain. In step one, a "profile" of conventional single-assay random forest ...
Drug Discovery Logistic Models Dose-Response Relationship, Drug Machine Learning Algorithms Biological Assay Inhibitory Concentration 50 Quantitative Structure-Activity Relationship Proteins Models, Chemical
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Orthogonal projection to latent structures and first derivative for manipulation of PLSR and SVR chemometric models' prediction: A case study.

PloS one Sep 25, 2019
Novel manipulations of the well-established multivariate calibration models namely; partial least square regression (PLSR) and support vector regression (SVR) are introduced in the presented comparative study. Two preprocessing methods comprising fir...
Drug Contamination Least-Squares Analysis Support Vector Machine Models, Chemical Pyridostigmine Bromide Spectrophotometry, Ultraviolet Models, Statistical Calibration
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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...
Models, Chemical Ligands Drug Design Humans Quantitative Structure-Activity Relationship Molecular Conformation Drug Discovery Neural Networks, Computer Small Molecule Libraries Proteins
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Could deep learning in neural networks improve the QSAR models?

SAR and QSAR in environmental research Aug 28, 2019
Assessing chemical toxicity is a multidisciplinary process, traditionally involving in vivo, in vitro and in silico tests. Currently, toxicological goal is to reduce new tests on chemicals, exploiting all information yet available. Recent advancement...
Mutagens Quantitative Structure-Activity Relationship Neural Networks, Computer Deep Learning Models, Chemical
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Energy-Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach.

ACS combinatorial science Aug 21, 2019
There is growing interest in estimating quantum observables while circumventing expensive computational overhead for facile in silico materials screening. Machine learning (ML) methods are implemented to perform such calculations in shorter times. He...
Computer Simulation Organic Chemicals Machine Learning Thermodynamics Quantum Theory Small Molecule Libraries Ions Inorganic Chemicals Molecular Structure Databases, Chemical Models, Chemical
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Learning To Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and Catalyst.

Journal of chemical information and modeling Aug 19, 2019
Reaction databases provide a great deal of useful information to assist planning of experiments but do not provide any interpretation or chemical concepts to accompany this information. In this work, reactions are labeled with experimental conditions...
Solvents Chemistry Techniques, Synthetic Cycloaddition Reaction Catalysis Models, Chemical Humans Machine Learning
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Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm.

Chemosphere Jul 30, 2019
This study aimed to model and optimize pyrene removal from the soil contaminated by sorghum bicolor plant using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) with Genetic Algorithm (GA) approach. Here, the effects of indole a...
Neural Networks, Computer Soil Pollutants Indoleacetic Acids Algorithms Models, Chemical Pyrenes Soil Biodegradation, Environmental
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A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability.

Molecules (Basel, Switzerland) Jun 30, 2019
Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical ch...
Ligands HIV Integrase Models, Chemical HIV-1 Machine Learning Computer Simulation
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