What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
Peter Willett mainly investigates Similarity, Data mining, Artificial intelligence, Pattern recognition and Algorithm.
His Similarity research incorporates themes from Fragment, Similarity measure, Sensor fusion and Virtual screening.
His Data mining research includes elements of Database, k-nearest neighbors algorithm, Simple, Hierarchical clustering and Document clustering.
The Artificial intelligence study combines topics in areas such as Machine learning, Binary number and Natural language processing.
His Algorithm research is multidisciplinary, incorporating elements of Theoretical computer science, Representation, Graph similarity, Subgraph isomorphism problem and Range.
His study in Biological system is interdisciplinary in nature, drawing from both Searching the conformational space for docking, DOCK, Small molecule binding and Evolution strategy.
His most cited work include:
- Development and validation of a genetic algorithm for flexible docking. (4405 citations)
- Chemical Similarity Searching (1302 citations)
- Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. (1269 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Similarity, Data mining, Information retrieval, Artificial intelligence and Algorithm.
His research in Similarity intersects with topics in Similarity measure, Virtual screening, Fingerprint and Sensor fusion.
His work deals with themes such as Fragment, Selection, Cluster analysis, Chemical database and Cheminformatics, which intersect with Data mining.
His work on Document retrieval as part of general Information retrieval research is frequently linked to Information system, thereby connecting diverse disciplines of science.
The various areas that Peter Willett examines in his Artificial intelligence study include Natural language processing, Machine learning and Pattern recognition.
His Algorithm research integrates issues from Subgraph isomorphism problem, Genetic algorithm, Theoretical computer science and Set.
He most often published in these fields:
- Similarity (19.65%)
- Data mining (19.25%)
- Information retrieval (19.06%)
What were the highlights of his more recent work (between 2009-2020)?
- Similarity (19.65%)
- Library science (7.86%)
- Cheminformatics (5.89%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Similarity, Library science, Cheminformatics, Data mining and Virtual screening.
The Similarity study which covers Fingerprint that intersects with Representation and Set.
His study in the fields of Citation, Information science and Bibliometrics under the domain of Library science overlaps with other disciplines such as Web of science and Bibliometric analysis.
His studies in Cheminformatics integrate themes in fields like Subgraph isomorphism problem, Structure, Information retrieval, Combinatorial chemistry and Data science.
His Data mining study incorporates themes from Simple and Cluster analysis.
His Virtual screening study combines topics from a wide range of disciplines, such as Algorithm and Fragment.
Between 2009 and 2020, his most popular works were:
- Combination of Similarity Rankings Using Data Fusion (109 citations)
- Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets. (104 citations)
- Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity. (91 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Similarity, Data mining, Information retrieval, Sensor fusion and Artificial intelligence.
His Similarity study integrates concerns from other disciplines, such as Fingerprint, Similarity measure, Virtual screening and Cheminformatics.
As a member of one scientific family, he mostly works in the field of Virtual screening, focusing on Nearest neighbor search and, on occasion, Inverse and Algorithm.
His studies deal with areas such as Hierarchical clustering, Correlation clustering, Cluster analysis, Simple and Single-linkage clustering as well as Data mining.
The concepts of his Information retrieval study are interwoven with issues in Regression analysis and DrugBank.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Virtual network, Square root and Pattern recognition.
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