What is he best known for?
The fields of study he is best known for:
- Enzyme
- Gene
- Organic chemistry
Kaixian Chen mostly deals with Stereochemistry, Biochemistry, Protein structure, Binding site and Organic chemistry.
Kaixian Chen combines topics linked to Rational design with his work on Stereochemistry.
His Protein structure research is multidisciplinary, incorporating elements of Crystallography, Molecular biology, Peptide sequence and Dimer.
His work carried out in the field of Binding site brings together such families of science as Active site, Plasma protein binding, Hydrogen bond and Molecular dynamics.
In the subject of general Organic chemistry, his work in Galactoside, Aryl, Catalysis and Corrosion is often linked to Glucoside, thereby combining diverse domains of study.
His Docking research integrates issues from Computational biology, DOCK and Dissociation constant.
His most cited work include:
- Predicting protein-protein interactions based only on sequences information. (599 citations)
- TarFisDock: a web server for identifying drug targets with docking approach (264 citations)
- PDTD: a web-accessible protein database for drug target identification (217 citations)
What are the main themes of his work throughout his whole career to date?
Stereochemistry, Biochemistry, Binding site, Molecular model and Combinatorial chemistry are his primary areas of study.
His Stereochemistry study combines topics in areas such as Structure–activity relationship and Hydrogen bond.
His Biochemistry study is mostly concerned with Enzyme, IC50, In vitro and Plasma protein binding.
Binding site connects with themes related to Protein structure in his study.
His work deals with themes such as Crystallography and Biophysics, which intersect with Protein structure.
His Combinatorial chemistry research focuses on subjects like Catalysis, which are linked to Aryl.
He most often published in these fields:
- Stereochemistry (29.69%)
- Biochemistry (22.93%)
- Binding site (11.35%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (8.95%)
- Biochemistry (22.93%)
- Machine learning (5.90%)
In recent papers he was focusing on the following fields of study:
Kaixian Chen mainly focuses on Artificial intelligence, Biochemistry, Machine learning, Drug discovery and IC50.
In general Artificial intelligence, his work in Deep learning is often linked to Value, Block and Protocol linking many areas of study.
His work in Biochemistry is not limited to one particular discipline; it also encompasses Anti-inflammatory.
His research investigates the connection between Machine learning and topics such as Kinome that intersect with issues in Polypharmacology and In silico.
The concepts of his Drug discovery study are interwoven with issues in Self attention, Peptide sequence, Chemical synthesis and Computational biology.
His Nuclear receptor research is multidisciplinary, incorporating perspectives in Virtual screening and Small molecule.
Between 2018 and 2021, his most popular works were:
- Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism. (39 citations)
- Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism. (39 citations)
- The dynamic conformational landscape of the protein methyltransferase SETD8. (20 citations)
In his most recent research, the most cited papers focused on:
- Enzyme
- Gene
- Organic chemistry
Kaixian Chen mainly investigates Artificial intelligence, Machine learning, Drug discovery, Deep learning and Stereochemistry.
His Artificial intelligence study also includes fields such as
- Polypharmacology, Small molecule and In silico most often made with reference to Profiling,
- Kinome which is related to area like Drug repositioning.
Kaixian Chen works mostly in the field of Machine learning, limiting it down to concerns involving Virtual screening and, occasionally, Bromodomain, Förster resonance energy transfer, Transcription factor and Computational biology.
While the research belongs to areas of Drug discovery, Kaixian Chen spends his time largely on the problem of Chemical biology, intersecting his research to questions surrounding Allosteric regulation, Biophysics, Enzyme catalysis, Molecular dynamics and Transferase.
His study in Deep learning is interdisciplinary in nature, drawing from both Artificial neural network and Atom.
His Stereochemistry research is multidisciplinary, incorporating elements of Protein arginine methyltransferase 5, Ic50 values and Amide.
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