What is he best known for?
The fields of study he is best known for:
- Enzyme
- Biochemistry
- Organic chemistry
Data mining, Drug discovery, Artificial intelligence, Support vector machine and Decision tree are his primary areas of study.
His work on Relational database as part of general Data mining study is frequently linked to Chemical safety, Open source and Critical rate, therefore connecting diverse disciplines of science.
His Drug discovery study integrates concerns from other disciplines, such as Quantitative structure–activity relationship, Inference and Drug repositioning.
His Drug repositioning research includes themes of Drug target, In vitro and Bioinformatics.
Weihua Li combines subjects such as Machine learning, ADME, Virtual screening and Pattern recognition with his study of Artificial intelligence.
His work in Decision tree addresses subjects such as Naive Bayes classifier, which are connected to disciplines such as Test set, PubChem, Artificial neural network and Cross-validation.
His most cited work include:
- admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. (688 citations)
- Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference (495 citations)
- admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. (154 citations)
What are the main themes of his work throughout his whole career to date?
Weihua Li mainly focuses on Stereochemistry, Drug discovery, Computational biology, Molecular dynamics and Artificial intelligence.
His work is dedicated to discovering how Stereochemistry, Receptor are connected with Nuclear receptor and Biophysics and other disciplines.
In his work, Drug repositioning is strongly intertwined with Inference, which is a subfield of Drug discovery.
His Computational biology research focuses on External validation and how it relates to Information gain.
His research integrates issues of Machine learning, Molecular descriptor and Pattern recognition in his study of Artificial intelligence.
His Support vector machine study combines topics from a wide range of disciplines, such as Decision tree, Data mining and Random forest.
He most often published in these fields:
- Stereochemistry (23.93%)
- Drug discovery (19.63%)
- Computational biology (19.02%)
What were the highlights of his more recent work (between 2018-2021)?
- Computational biology (19.02%)
- Drug discovery (19.63%)
- Artificial intelligence (13.50%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Computational biology, Drug discovery, Artificial intelligence, Machine learning and Applicability domain.
His Computational biology research is multidisciplinary, relying on both External validation and Drug repositioning.
Weihua Li has researched Drug discovery in several fields, including Inference, MEDLINE, Drug and Metabolism.
The Inference study combines topics in areas such as Text mining and Data mining.
His Drug study integrates concerns from other disciplines, such as Toxicology testing, Bioinformatics and Liver injury.
His Test set and Molecular descriptor study in the realm of Machine learning interacts with subjects such as Recommendation model.
Between 2018 and 2021, his most popular works were:
- admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. (154 citations)
- ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness. (42 citations)
- Correction to "admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties". (10 citations)
In his most recent research, the most cited papers focused on:
- Enzyme
- Biochemistry
- Organic chemistry
His main research concerns Drug discovery, Computational biology, Drug, Information retrieval and MEDLINE.
By researching both Drug discovery and Lead, he produces research that crosses academic boundaries.
His Computational biology research is multidisciplinary, incorporating elements of Data imbalance, Single label, Resampling and Xenobiotic.
His Drug study combines topics in areas such as Toxicology testing, Bioinformatics and Liver injury.
Weihua Li undertakes multidisciplinary studies into Information retrieval and Web service in his work.
Weihua Li focuses mostly in the field of Big data, narrowing it down to topics relating to Applicability domain and, in certain cases, Information gain, Predictive modelling, External validation and In vivo tests.
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