What is he best known for?
The fields of study he is best known for:
His primary areas of study are Biochemistry, Small molecule, High-throughput screening, Structure–activity relationship and Drug discovery.
The study of Biochemistry is intertwined with the study of Stereochemistry in a number of ways.
His Small molecule research includes themes of Base excision repair, Molecular biology, FOXM1, Forkhead Transcription Factors and Transcription.
His High-throughput screening research is multidisciplinary, incorporating perspectives in Autofluorescence, Nanotechnology, Beta-Lactamase Inhibitors, Fluorescence spectrometry and Combinatorial chemistry.
His work deals with themes such as Toxicity, Stress response pathway, Molecular model and Pharmacology, which intersect with Structure–activity relationship.
His research in Drug discovery intersects with topics in Computational biology, Cell function and Molecular targets.
His most cited work include:
- Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. (623 citations)
- High-throughput screening assays for the identification of chemical probes (458 citations)
- Identification of small-molecule inhibitors of Zika virus infection and induced neural cell death via a drug repurposing screen (396 citations)
What are the main themes of his work throughout his whole career to date?
Anton Simeonov mainly investigates Biochemistry, Small molecule, Computational biology, Drug discovery and High-throughput screening.
Anton Simeonov interconnects Molecular biology and Stereochemistry in the investigation of issues within Biochemistry.
Anton Simeonov combines subjects such as DNA damage and Cell biology with his study of Small molecule.
In most of his Computational biology studies, his work intersects topics such as High-Throughput Screening Assays.
He most often published in these fields:
- Biochemistry (28.67%)
- Small molecule (17.25%)
- Computational biology (11.42%)
What were the highlights of his more recent work (between 2019-2021)?
- Computational biology (11.42%)
- Drug (6.53%)
- Pharmacology (10.02%)
In recent papers he was focusing on the following fields of study:
Computational biology, Drug, Pharmacology, Drug repositioning and Cancer research are his primary areas of study.
The study incorporates disciplines such as Viral replication, In silico, Small molecule and Proteasome in addition to Computational biology.
His Small molecule study incorporates themes from Phosphatidylinositol, Kinase, Phosphatidylinositol 5-phosphate, Structure–activity relationship and Drug discovery.
His research in the fields of Combination therapy overlaps with other disciplines such as Camostat.
His Cytotoxicity research entails a greater understanding of Biochemistry.
The concepts of his Biochemistry study are interwoven with issues in Schistosomiasis and Praziquantel.
Between 2019 and 2021, his most popular works were:
- Remdesivir: A Review of Its Discovery and Development Leading to Emergency Use Authorization for Treatment of COVID-19 (196 citations)
- Fruitful Neutralizing Antibody Pipeline Brings Hope To Defeat SARS-Cov-2. (37 citations)
- Mannose receptor (CD206) activation in tumor-associated macrophages enhances adaptive and innate antitumor immune responses (34 citations)
In his most recent research, the most cited papers focused on:
His primary areas of investigation include Drug repositioning, Severe acute respiratory syndrome coronavirus 2, Pharmacology, Clinical trial and Coronavirus.
His research integrates issues of Cytopathic effect and Approved drug in his study of Drug repositioning.
His work deals with themes such as Pandemic and Emergency Use Authorization, which intersect with Severe acute respiratory syndrome coronavirus 2.
His Pharmacology research is multidisciplinary, relying on both Proteases, Gabexate, Nafamostat, Protease and Viral entry.
In his work, Biochemistry is strongly intertwined with In vivo, which is a subfield of Cell culture.
He interconnects Luciferase, Chemical structure and High-throughput screening in the investigation of issues within Computational biology.
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