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.
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.
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
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.
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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Beware of q2
Alexander Golbraikh;Alexander Tropsha.
Journal of Molecular Graphics & Modelling (2002)
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
Alexander Tropsha;Paola Gramatica;Vijay K. Gombar.
Qsar & Combinatorial Science (2003)
Best Practices for QSAR Model Development, Validation, and Exploitation.
Molecular Informatics (2010)
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)
Rational selection of training and test sets for the development of validated QSAR models.
Alexander Golbraikh;Min Shen;Zhiyan Xiao;Yun De Xiao.
Journal of Computer-aided Molecular Design (2003)
Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
Denis Fourches;Eugene N. Muratov;Alexander Tropsha.
Journal of Chemical Information and Modeling (2010)
Deep reinforcement learning for de novo drug design
Mariya Popova;Mariya Popova;Mariya Popova;Olexandr Isayev;Alexander E Tropsha.
Science Advances (2018)
Novel variable selection quantitative structure--property relationship approach based on the k-nearest-neighbor principle
Weifan Zheng;Alexander Tropsha.
Journal of Chemical Information and Computer Sciences (2000)
Chemical Basis of Interactions Between Engineered Nanoparticles and Biological Systems
Qingxin Mu;Guibin Jiang;Lingxin Chen;Hongyu Zhou;Hongyu Zhou.
Chemical Reviews (2014)
Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
David L J Alexander;Alexander Tropsha;David Alan Winkler.
Journal of Chemical Information and Modeling (2015)
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