Literature Review: Graph Kernels in Chemoinformatics
The purpose of this review is to introduce the reader to graph kernels and the corresponding literature, with an emphasis on those with direct application to chemoinformatics. Graph kernels are functions that allow for the inference of properties of molecules and compounds, which can help with tasks such as finding suitable compounds in drug design. The use of kernel methods is but one particular way two quantify similarity between graphs. We restrict our discussion to this one method, although popular alternatives have emerged in recent years, most notably graph neural networks.
Code (0)
등록된 구현이 없습니다.
Tasks
Drug DesignSimilar Papers 제목 키워드 기반
Graph Kernels: State-of-the-Art and Future Challenges
Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis. Over the last two decades, numerous graph kernels, i.e…
regressionGraph Capsule Convolutional Neural Networks
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, …
Deep LearningGeneral ClassificationGraph ClassificationConformal Predictors for Compound Activity Prediction
The paper presents an application of Conformal Predictors to a chemoinformatics problem of identifying activities of chemical compounds. The paper addresses some specific challenges of this domain: a large number of comp…
Activity PredictionConformal PredictionPredictionPredictive Chemistry Augmented with Text Retrieval
This paper focuses on using natural language descriptions to enhance predictive models in the chemistry field. Conventionally, chemoinformatics models are trained with extensive structured data manually extracted from th…
molecular representationRetrievalRetrosynthesisText RetrievalMetropolis Algorithms for Representative Subgraph Sampling
While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and the Internet are now generating graph data with thousands and millions of nodes. Hence data mining faces the algorithmic …