Machine learning methods in drug discovery
The advancements of information technology and related processing techniques have
created a fertile base for progress in many scientific fields and industries. In the fields of drug …
created a fertile base for progress in many scientific fields and industries. In the fields of drug …
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 …
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 …
Comparison of the predictive performance and interpretability of random forest and linear models on benchmark data sets
RL Marchese Robinson, A Palczewska… - Journal of chemical …, 2017 - ACS Publications
The ability to interpret the predictions made by quantitative structure–activity relationships
(QSARs) offers a number of advantages. While QSARs built using nonlinear modeling …
(QSARs) offers a number of advantages. While QSARs built using nonlinear modeling …
Insights into Machine Learning-based approaches for Virtual Screening in Drug Discovery: Existing strategies and streamlining through FP-CADD
Background: Machine learning is an active area of research in computer science by the
availability of big data collection of all sorts prompting interest in the development of novel …
availability of big data collection of all sorts prompting interest in the development of novel …
Prediction of G protein‐coupled receptors with SVM‐prot features and random forest
G protein‐coupled receptors (GPCRs) are the largest receptor superfamily. In this paper, we
try to employ physical‐chemical properties, which come from SVM‐Prot, to represent GPCR …
try to employ physical‐chemical properties, which come from SVM‐Prot, to represent GPCR …
Interpreting linear support vector machine models with heat map molecule coloring
L Rosenbaum, G Hinselmann, A Jahn, A Zell - Journal of Cheminformatics, 2011 - Springer
Background Model-based virtual screening plays an important role in the early drug
discovery stage. The outcomes of high-throughput screenings are a valuable source for …
discovery stage. The outcomes of high-throughput screenings are a valuable source for …
Chemoinformatics: a view of the field and current trends in method development
M Vogt, J Bajorath - Bioorganic & medicinal chemistry, 2012 - Elsevier
The chemoinformatics field continues to evolve at the interface between computer science
and chemistry. Chemical information and computational approaches in pharmaceutical …
and chemistry. Chemical information and computational approaches in pharmaceutical …
Computational drug repositioning based on the relationships between substructure–indication
J Yang, D Zhang, L Liu, G Li, Y Cai… - Briefings in …, 2021 - academic.oup.com
At present, computational methods for drug repositioning are mainly based on the whole
structures of drugs, which limits the discovery of new functions due to the similarities …
structures of drugs, which limits the discovery of new functions due to the similarities …
Artificial intelligence and bioinformatics
J Nicolas - A Guided Tour of Artificial Intelligence Research …, 2020 - Springer
The chapter shines a light on the strong links shared by Artificial intelligence and
Bioinformatics since many years. Bioinformatics offers many NP-hard problems that are …
Bioinformatics since many years. Bioinformatics offers many NP-hard problems that are …