What is he best known for?
The fields of study he is best known for:
His primary areas of investigation include Pharmacophore, Quantitative structure–activity relationship, Computational biology, Drug discovery and Stereochemistry.
His work deals with themes such as Pregnane X receptor, Structure–activity relationship and Transporter, which intersect with Pharmacophore.
His work carried out in the field of Computational biology brings together such families of science as Bioinformatics, Proteomics, Computational model, In silico and Test set.
The study incorporates disciplines such as Data science and Drug in addition to Bioinformatics.
His studies in Drug discovery integrate themes in fields like Nanotechnology, Pharmacology and Biological data.
Sean Ekins has included themes like Protein structure, Substrate and Binding site in his Stereochemistry study.
His most cited work include:
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling (352 citations)
- Pathway mapping tools for analysis of high content data. (234 citations)
- Progress in predicting human ADME parameters in silico. (210 citations)
What are the main themes of his work throughout his whole career to date?
Sean Ekins focuses on Drug discovery, Computational biology, Pharmacophore, Pharmacology and Biochemistry.
His Drug discovery study combines topics in areas such as ADME, Cheminformatics, Artificial intelligence and Tuberculosis, Mycobacterium tuberculosis.
His Computational biology research includes themes of Bioinformatics, Computational model, Pregnane X receptor, Drug and In silico.
His Pregnane X receptor research integrates issues from Receptor and Docking.
The various areas that Sean Ekins examines in his Pharmacophore study include Quantitative structure–activity relationship, Molecular descriptor and Enzyme.
His Pharmacology research includes elements of In vitro and In vivo.
He most often published in these fields:
- Drug discovery (26.65%)
- Computational biology (26.43%)
- Pharmacophore (18.72%)
What were the highlights of his more recent work (between 2018-2021)?
- Virology (6.39%)
- Machine learning (8.15%)
- Artificial intelligence (9.03%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Virology, Machine learning, Artificial intelligence, Tilorone and Ebola virus.
His biological study spans a wide range of topics, including chEMBL and Drug discovery.
His Drug discovery research is multidisciplinary, incorporating elements of Disease and Drug resistance.
His Training set, Bayesian probability and Bayesian inference study in the realm of Artificial intelligence connects with subjects such as Sialidosis.
His Tilorone research incorporates elements of In vivo and Pyronaridine.
His research in In vivo tackles topics such as Drug which are related to areas like Computational biology.
Between 2018 and 2021, his most popular works were:
- Exploiting machine learning for end-to-end drug discovery and development (114 citations)
- Déjà vu: Stimulating open drug discovery for SARS-CoV-2. (41 citations)
- Repurposing the antimalarial pyronaridine tetraphosphate to protect against Ebola virus infection. (28 citations)
In his most recent research, the most cited papers focused on:
The scientist’s investigation covers issues in Virology, Ebola virus, Repurposing, Virus and Tilorone.
His research integrates issues of Drug repositioning, Feline calicivirus, In vivo and Pyronaridine in his study of Ebola virus.
His In vivo research is multidisciplinary, incorporating perspectives in In vitro and Drug.
His Virus study incorporates themes from Drug resistance and Coronavirus.
His research investigates the connection with Pandemic and areas like Global health which intersect with concerns in Drug discovery.
Borrowing concepts from Exploit, Sean Ekins weaves in ideas under Drug discovery.
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