What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Organic chemistry
- Molecule
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.
His most cited work include:
- ITERATIVE PARTIAL EQUALIZATION OF ORBITAL ELECTRONEGATIVITY – A RAPID ACCESS TO ATOMIC CHARGES (2906 citations)
- Virtual computational chemistry laboratory - design and description (1101 citations)
- QSAR Modeling: Where have you been? Where are you going to? (764 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Molecule (13.73%)
- Artificial neural network (13.13%)
- Artificial intelligence (12.84%)
What were the highlights of his more recent work (between 2005-2020)?
- Cheminformatics (5.07%)
- Artificial intelligence (12.84%)
- Combinatorial chemistry (4.78%)
In recent papers he was focusing on the following fields of study:
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.
Between 2005 and 2020, his most popular works were:
- QSAR Modeling: Where have you been? Where are you going to? (764 citations)
- Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information (229 citations)
- Structure and reaction based evaluation of synthetic accessibility (102 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Organic chemistry
- Enzyme
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
- Chemist, Biological data and Scale most often made with reference to Field,
- Data science which is related to area like Chemometrics and Combinatorial chemistry.
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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