A survey on graph kernels
Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …
classification tasks on graphs. This survey gives a comprehensive overview of techniques …
Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries
The global spread of COVID-19 has raised the importance of pharmaceutical drug
development as intractable and hot research. Develo** new drug molecules to overcome …
development as intractable and hot research. Develo** new drug molecules to overcome …
DeepTox: toxicity prediction using deep learning
The Tox21 Data Challenge has been the largest effort of the scientific community to compare
computational methods for toxicity prediction. This challenge comprised 12,000 …
computational methods for toxicity prediction. This challenge comprised 12,000 …
Machine-learning approaches in drug discovery: methods and applications
A Lavecchia - Drug discovery today, 2015 - Elsevier
Highlights•We review machine learning methods/tools relevant to ligand-based virtual
screening.•Machine learning methods classify compounds and predict new active …
screening.•Machine learning methods classify compounds and predict new active …
Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets
Although a wide variety of machine learning (ML) algorithms have been utilized to learn
quantitative structure–activity relationships (QSARs), there is no agreed single best …
quantitative structure–activity relationships (QSARs), there is no agreed single best …
Supervised prediction of drug–target interactions using bipartite local models
Motivation: In silico prediction of drug–target interactions from heterogeneous biological
data is critical in the search for drugs for known diseases. This problem is currently being …
data is critical in the search for drugs for known diseases. This problem is currently being …
Subgraph matching kernels for attributed graphs
We propose graph kernels based on subgraph matchings, ie structure-preserving bijections
between subgraphs. While recently proposed kernels based on common subgraphs (Wale …
between subgraphs. While recently proposed kernels based on common subgraphs (Wale …
Protein-ligand interaction prediction: an improved chemogenomics approach
Motivation: Predicting interactions between small molecules and proteins is a crucial step to
decipher many biological processes, and plays a critical role in drug discovery. When no …
decipher many biological processes, and plays a critical role in drug discovery. When no …
Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?
This paper is focused on modern approaches to machine learning, most of which are as yet
used infrequently or not at all in chemoinformatics. Machine learning methods are …
used infrequently or not at all in chemoinformatics. Machine learning methods are …
Support vector machines for drug discovery
K Heikamp, J Bajorath - Expert opinion on drug discovery, 2014 - Taylor & Francis
Introduction: Support vector machines (SVMs) are supervised machine learning algorithms
for binary class label prediction and regression-based prediction of property values. In …
for binary class label prediction and regression-based prediction of property values. In …