What is he best known for?
The fields of study he is best known for:
- Enzyme
- Gene
- Artificial intelligence
His primary areas of study are Quantitative structure–activity relationship, Artificial intelligence, Machine learning, Applicability domain and k-nearest neighbors algorithm.
The Quantitative structure–activity relationship study combines topics in areas such as Virtual screening, Data mining, Set, Feature selection and Test set.
His work in the fields of Artificial intelligence, such as Support vector machine, Range and Reinforcement learning, overlaps with other areas such as Design methods.
His Machine learning research incorporates themes from Property, Representation and Nanotechnology.
Alexander Tropsha usually deals with Applicability domain and limits it to topics linked to Chemical database and Pharmacophore.
The study incorporates disciplines such as Amino acid, Manufactured nanoparticles, Biological system and Molecular descriptor in addition to k-nearest neighbors algorithm.
His most cited work include:
- Beware of q2 (2599 citations)
- The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models (1478 citations)
- Best Practices for QSAR Model Development, Validation, and Exploitation. (910 citations)
What are the main themes of his work throughout his whole career to date?
Alexander Tropsha mainly investigates Quantitative structure–activity relationship, Computational biology, Artificial intelligence, Virtual screening and Machine learning.
He works in the field of Quantitative structure–activity relationship, focusing on Applicability domain in particular.
His work on Set expands to the thematically related Applicability domain.
His study explores the link between Computational biology and topics such as Protein structure that cross with problems in Tetrahedron.
His Artificial intelligence research incorporates elements of Property and Pattern recognition.
In his research on the topic of Virtual screening, Data science is strongly related with Cheminformatics.
He most often published in these fields:
- Quantitative structure–activity relationship (39.80%)
- Computational biology (21.71%)
- Artificial intelligence (16.78%)
What were the highlights of his more recent work (between 2018-2021)?
- Computational biology (21.71%)
- Repurposing (2.63%)
- Graph (3.62%)
In recent papers he was focusing on the following fields of study:
Alexander Tropsha mostly deals with Computational biology, Repurposing, Graph, Quantitative structure–activity relationship and Artificial intelligence.
His Computational biology research is multidisciplinary, relying on both Animal testing, False positive paradox, In silico and Cheminformatics.
His Graph research also works with subjects such as
- Knowledge graph which intersects with area such as Identifier, Graph query and Theoretical computer science,
- World Wide Web, which have a strong connection to MEDLINE, Relevance and UniProt.
Alexander Tropsha is studying Quantitative structure, which is a component of Quantitative structure–activity relationship.
His Artificial intelligence study combines topics in areas such as Cancer and Machine learning.
His work deals with themes such as Virtual screening, Docking and Knowledge acquisition, which intersect with Drug repositioning.
Between 2018 and 2021, his most popular works were:
- QSAR without borders (83 citations)
- CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. (30 citations)
- NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment (21 citations)
In his most recent research, the most cited papers focused on:
- Enzyme
- Gene
- Artificial intelligence
His primary areas of investigation include Quantitative structure–activity relationship, Cheminformatics, Computational biology, Combinatorial chemistry and Drug repositioning.
His Quantitative structure–activity relationship research is multidisciplinary, incorporating elements of Computational chemistry and Data science.
His Cheminformatics research integrates issues from Chemical space, Virtual screening, Epigenetics and Chemogenomics.
He has researched Computational biology in several fields, including Ribosomal RNA, Biosynthesis, Enzyme and Drug discovery.
His biological study spans a wide range of topics, including Ammonium chloride and Solubility.
His Drug repositioning research includes themes of Docking and DrugBank.
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