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Predicting Melting Points of Organic Molecules: Applications to Aqueous Solubility Prediction Using the General Solubility Equation.

Molecular informatics
In this work we make predictions of several important molecular properties of academic and industrial importance to seek answers to two questions: 1) Can we apply efficient machine learning techniques, using inexpensive descriptors, to predict meltin...

Quantitative Regression Models for the Prediction of Chemical Properties by an Efficient Workflow.

Molecular informatics
Rapid safety assessment is more and more needed for the increasing chemicals both in chemical industries and regulators around the world. The traditional experimental methods couldn't meet the current demand any more. With the development of the info...

BiNChE: a web tool and library for chemical enrichment analysis based on the ChEBI ontology.

BMC bioinformatics
BACKGROUND: Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biol...

Determination of Meta-Parameters for Support Vector Machine Linear Combinations.

Molecular informatics
Support vector machines (SVMs) are among the most popular machine learning methods for compound classification and other chemoinformatics tasks such as, for example, the prediction of ligand-target pairs or compound activity profiles. Depending on th...

GTM-Based QSAR Models and Their Applicability Domains.

Molecular informatics
In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the l...

Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks.

SAR and QSAR in environmental research
P-glycoprotein (P-gp) is an ATP binding cassette (ABC) transporter that helps to protect several certain human organs from xenobiotic exposure. This efflux pump is also responsible for multi-drug resistance (MDR), an issue of the chemotherapy approac...

Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity.

Journal of chemical information and modeling
Fingerprint methods applied to molecules have proven to be useful for similarity determination and as inputs to machine-learning models. Here, we present the development of a new fingerprint for chemical reactions and validate its usefulness in build...

Active-learning strategies in computer-assisted drug discovery.

Drug discovery today
High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the s...

SuperPred 3.0: drug classification and target prediction-a machine learning approach.

Nucleic acids research
Since the last published update in 2014, the SuperPred webserver has been continuously developed to offer state-of-the-art models for drug classification according to ATC classes and target prediction. For the first time, a thoroughly filtered ATC da...

Machine learned calibrations to high-throughput molecular excited state calculations.

The Journal of chemical physics
Understanding the excited state properties of molecules provides insight into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to inc...