What is he best known for?
The fields of study he is best known for:
- Gene
- Enzyme
- Quantum mechanics
Protein structure, Protein structure prediction, Crystallography, Threading and Protein folding are his primary areas of study.
The concepts of his Protein structure study are interwoven with issues in Protein tertiary structure, Pair potential and Binding site.
His Protein structure prediction research is multidisciplinary, relying on both Root-mean-square deviation, Energy landscape, Biological system, Protein Data Bank and Algorithm.
His biological study spans a wide range of topics, including Chemical physics, Lattice protein and Protein secondary structure.
His Threading research is multidisciplinary, incorporating perspectives in Protein superfamily, Genetics, Ab initio and Computational biology.
His work deals with themes such as Random coil, Evolutionary information, Crambin, Globular protein and Protein–protein interaction prediction, which intersect with Protein folding.
His most cited work include:
- TM-align: a protein structure alignment algorithm based on the TM-score. (1705 citations)
- From genes to protein structure and function: novel applications of computational approaches in the genomic era (1444 citations)
- Scoring function for automated assessment of protein structure template quality (1215 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Protein structure, Crystallography, Protein structure prediction, Protein folding and Computational biology.
Jeffrey Skolnick combines subjects such as Peptide sequence, Protein tertiary structure, Biological system and Artificial intelligence with his study of Protein structure.
His Crystallography research incorporates themes from Chemical physics and Protein secondary structure.
His Protein structure prediction study integrates concerns from other disciplines, such as Homology modeling, Ab initio, Protein Data Bank, Threading and Algorithm.
His studies deal with areas such as Statistical physics and Lattice as well as Protein folding.
His research investigates the connection between Computational biology and topics such as Genetics that intersect with issues in Structural genomics.
He most often published in these fields:
- Protein structure (30.81%)
- Crystallography (29.38%)
- Protein structure prediction (22.75%)
What were the highlights of his more recent work (between 2012-2021)?
- Computational biology (17.30%)
- Protein structure (30.81%)
- Drug discovery (3.32%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Computational biology, Protein structure, Drug discovery, Biochemistry and Dihydrofolate reductase.
The various areas that Jeffrey Skolnick examines in his Computational biology study include Virtual screening, Docking, Protein Data Bank and Ligand.
His Protein Data Bank study incorporates themes from Glycoprotein, Glycan, Crystallography and Protein Data Bank.
Jeffrey Skolnick studies Protein structure prediction which is a part of Protein structure.
His research in Protein structure prediction intersects with topics in Algorithm and Structural genomics.
Jeffrey Skolnick works mostly in the field of Drug discovery, limiting it down to topics relating to Combinatorial chemistry and, in certain cases, Antibacterial activity.
Between 2012 and 2021, his most popular works were:
- APoc: large-scale identification of similar protein pockets (77 citations)
- A Comprehensive Survey of Small-Molecule Binding Pockets in Proteins (71 citations)
- Insights into Disease-Associated Mutations in the Human Proteome through Protein Structural Analysis. (70 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Enzyme
- Quantum mechanics
His primary areas of study are Protein structure, Computational biology, Drug discovery, Biochemistry and Plasma protein binding.
His work deals with themes such as Amino acid, Virtual screening, Data mining, Ligand and Binding site, which intersect with Protein structure.
His Computational biology research is multidisciplinary, incorporating perspectives in Small number, Protein Data Bank and Function.
As a member of one scientific family, Jeffrey Skolnick mostly works in the field of Protein Data Bank, focusing on Glycan and, on occasion, Protein structure prediction.
His Protein structure prediction study combines topics in areas such as Algorithm and Protein tertiary structure.
His study explores the link between Plasma protein binding and topics such as Protein Data Bank that cross with problems in Sequence, Small molecule binding, Protein–protein interaction and Sequence alignment.
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