State-of-the-art in ligand-based virtual screening.

By Peter Ripphausen, Britta Nisius, Jürgen Bajorath. 2011

226 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Molecular Biology, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Glaucius Oliva
2. L. Ferreira
3. Ricardo N Dos Santos
4. Alisson Marques da Silva
5. Tiago Alves de Oliveira
6. Adriano Defini Andricopulo
7. Letícia C. Assis
8. Alex Gutterres Taranto

In Molecular Biology

1. Glaucius Oliva
2. L. Ferreira
3. Ricardo N Dos Santos
4. Alisson Marques da Silva
5. Tiago Alves de Oliveira
6. Adriano Defini Andricopulo
7. Tunca Dogan
8. Letícia C. Assis

In Computational Chemistry & Drug Discovery

1. Glaucius Oliva
2. Alisson Marques da Silva
3. Tiago Alves de Oliveira
4. Adriano Defini Andricopulo
5. Letícia C. Assis
6. L. Ferreira
7. Alex Gutterres Taranto
8. Eduardo Habib Bechelane Maia

Citations in the corpus

• Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “A number of reviews on LBVS methods are available in the literature [129,130].”
• Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, background): “Ligand-based methods sidestep the challenges of structurebased approaches and only require a set of molecules that are known to be active against a particular receptor or trigger a particular phenotype (11, 12).”
• Structure-Based Virtual Screening: From Classical to Artificial Intelligence (2020, background): “Therefore, it is a continuous challenge to find viable solutions that reduce the simulation runtime and simultaneously increase the accuracy of the simulations (Ripphausen et al., 2011).”
• The use of 2D fingerprint methods to support the assessment of structural similarity in orphan drug legislation (2014, unknown): “A characteristic of most of these studies is that they have focused on the identification of molecules that are similar to each other; in similarity-based virtual screening, for example, the aim is to identify those previously untested database structures that are most similar to a bioactive referen”
• Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases (2018, methodology): “[29, 33, 34], • Proteochemometric modeling (PCM) approach models the inter-”
• Accurate and efficient target prediction using a potency-sensitive influence-relevance voter (2015, unknown): “These include protein structure-based methods [13, 14], and ligand-based methods such as pharmacophore searching [3], substructural analysis [15], and similarity searching [4].”