AI Medical Compendium Topic

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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition.

Journal of chemical information and modeling
Inspired by natural language processing techniques, we here introduce Mol2vec, which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Like the Word2vec models, where vectors of closely related w...

Transparent predictive modelling of the twin screw granulation process using a compensated interval type-2 fuzzy system.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
In this research, a new systematic modelling framework which uses machine learning for describing the granulation process is presented. First, an interval type-2 fuzzy model is elicited in order to predict the properties of the granules produced by t...

Transductive Ridge Regression in Structure-activity Modeling.

Molecular informatics
In this article we consider the application of the Transductive Ridge Regression (TRR) approach to structure-activity modeling. An original procedure of the TRR parameters optimization is suggested. Calculations performed on 3 different datasets invo...

Generative Recurrent Networks for De Novo Drug Design.

Molecular informatics
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a...

Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction.

Journal of chemical information and modeling
Nanoparticles exhibit diverse structural and morphological features that are often interconnected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanopart...

Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.

Journal of chemical information and modeling
Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision1,2 and natural language processing.3,4 In the past four years, DNNs have also generated p...

Bias-Free Chemically Diverse Test Sets from Machine Learning.

ACS combinatorial science
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal an...

Comments on prediction of the aqueous solubility using the general solubility equation (GSE) versus a genetic algorithm and a support vector machine model.

Pharmaceutical development and technology
The general solubility equation (GSE) is the state-of-the-art method for estimating the aqueous solubilities of organic compounds. It is an extremely simple equation that expresses aqueous solubility as a function of only two inputs: the octanol-wate...

Identification of small molecules using accurate mass MS/MS search.

Mass spectrometry reviews
Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-base...

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

Scientific reports
Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large ...