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Quantitative Structure-Activity Relationship

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Machine Learning Estimation of Atom Condensed Fukui Functions.

Molecular informatics
To enable the fast estimation of atom condensed Fukui functions, machine learning algorithms were trained with databases of DFT pre-calculated values for ca. 23,000 atoms in organic molecules. The problem was approached as the ranking of atom types w...

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.

BioMed research international
Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM ...

Development of an informatics infrastructure for data exchange of biomolecular simulations: Architecture, data models and ontology.

SAR and QSAR in environmental research
Biomolecular simulations aim to simulate structure, dynamics, interactions, and energetics of complex biomolecular systems. With the recent advances in hardware, it is now possible to use more complex and accurate models, but also reach time scales t...

Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models.

Journal of theoretical biology
Signaling proteins are an important topic in drug development due to the increased importance of finding fast, accurate and cheap methods to evaluate new molecular targets involved in specific diseases. The complexity of the protein structure hinders...

Relevance Vector Machines: Sparse Classification Methods for QSAR.

Journal of chemical information and modeling
Sparse machine learning methods have provided substantial benefits to quantitative structure property modeling, as they make model interpretation simpler and generate models with improved predictivity. Sparsity is usually induced via Bayesian regular...

Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

Environmental science and pollution research international
Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and developmen...

Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets.

Journal of chemical information and modeling
To date, no systematic study has assessed the effect of random experimental errors on the predictive power of QSAR models. To address this shortage, we have benchmarked the noise sensitivity of 12 learning algorithms on 12 data sets (15,840 models in...

Probabilistic hazard assessment for skin sensitization potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships.

Journal of applied toxicology : JAT
Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse ...

Joint Toxicity of Lead, Chromium, Cobalt and Nickel to Photobacterium phosphoreum at No Observed Effect Concentration.

Bulletin of environmental contamination and toxicology
Joint toxicity of Pb2+, Cr3+, Co2+ and Ni2+ toward Photobacterium phosphoreum (Ph. phosphoreum) at the no observed effect concentration (NOEC) was determined through a factorial experiment. A neural network model was designed according to experimenta...