The scientist’s investigation covers issues in Molecule, Artificial neural network, Computational chemistry, Artificial intelligence and Set. Polarizability, Molecular physics and Physical chemistry is closely connected to Electronegativity in his research, which is encompassed under the umbrella topic of Molecule. The Artificial neural network study combines topics in areas such as Representation, Structure and Biological system.
Johann Gasteiger has researched Computational chemistry in several fields, including Branching, Matrix, Statistical physics, Naphthalene and Cyclohexane. His Artificial intelligence research incorporates themes from Process control, Partial least squares regression and Pattern recognition. His biological study spans a wide range of topics, including Topology, Information management and Data mining.
Molecule, Artificial neural network, Artificial intelligence, Computational chemistry and Stereochemistry are his primary areas of study. His study focuses on the intersection of Molecule and fields such as Electronegativity with connections in the field of Polarizability. His Artificial neural network research is multidisciplinary, incorporating perspectives in Representation, Structure and Biological system.
His research combines Set and Biological system. His work deals with themes such as Quantitative structure–activity relationship, Machine learning and Pattern recognition, which intersect with Artificial intelligence. His Computational chemistry study typically links adjacent topics like Organic chemistry.
Johann Gasteiger mostly deals with Cheminformatics, Artificial intelligence, Combinatorial chemistry, Data mining and Computational biology. He has included themes like Nanotechnology, Management science, Field, Analytical Chemistry and Data science in his Cheminformatics study. His Artificial intelligence research incorporates elements of Machine learning, Family Asteraceae and Pattern recognition.
His work in Data mining addresses issues such as Set, which are connected to fields such as Applicability domain and Variety. Johann Gasteiger interconnects Genetics, Feature selection, Metabolic pathway and Order in the investigation of issues within Computational biology. His studies in Quantitative structure–activity relationship integrate themes in fields like Computational chemistry and Molecule.
His scientific interests lie mostly in Data mining, Cheminformatics, Quantitative structure–activity relationship, Computational biology and Bioinformatics. His Data mining study incorporates themes from Structure, Set and Data set. His Cheminformatics research also works with subjects such as
His Quantitative structure–activity relationship study combines topics in areas such as Inverse, Multi-objective optimization, Mathematical optimization, Partial least squares regression and Range. His study on Synthetic biology is often connected to Extramural, Best practice, Praise and Microbial metabolism as part of broader study in Bioinformatics. His work in Expectation–maximization algorithm incorporates the disciplines of Artificial neural network, Test set, Machine learning and Artificial intelligence.
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ITERATIVE PARTIAL EQUALIZATION OF ORBITAL ELECTRONEGATIVITY – A RAPID ACCESS TO ATOMIC CHARGES
Johann Gasteiger;Mario Marsili.
Virtual computational chemistry laboratory - design and description
Igor V. Tetko;Johann Gasteiger;Roberto Todeschini;Andrea Mauri.
Journal of Computer-aided Molecular Design (2005)
QSAR Modeling: Where have you been? Where are you going to?
Artem Cherkasov;Eugene N. Muratov;Eugene N. Muratov;Denis Fourches;Alexandre Varnek.
Journal of Medicinal Chemistry (2014)
Neural Networks for Chemists: An Introduction
Jure Zupan;Johann Gasteiger.
Neural networks in chemistry and drug design
Jure Zupan;Johann Gasteiger.
Encyclopedia of computational chemistry
Peter R. Schreiner;Norman L. Allinger;Terry W. Clark;Johann Gasteiger.
Neural networks: A new method for solving chemical problems or just a passing phase?
J. Zupan;J. Gasteiger.
Analytica Chimica Acta (1991)
Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures
Jens Sadowski;Johann Gasteiger;Gerhard Klebe.
Journal of Chemical Information and Computer Sciences (1994)
Automatic generation of 3D-atomic coordinates for organic molecules
J. Gasteiger;C. Rudolph;J. Sadowski.
Tetrahedron Computer Methodology (1990)
Chemoinformatics: A Textbook
Johann Gasteiger;Thomas Engel.
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