Integrating virtual screening in lead discovery.

By Tudor I. Oprea, Hans Matter. 2004

240 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Materials Chemistry, Evaluation & Benchmarks, Computational Chemistry & Drug Discovery

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “Usually, VS strategies fall into two main types: (i) ligand-and (ii) structure-based virtual screening (LBVS and SBVS, respectively, Figure 5) [128].”
Combining docking and molecular dynamic simulations in drug design (2006, background): “10,11 Initially, libraries are ‘‘pre-ﬁltered’’ using a series of simple physicochemical descriptors to eliminate compounds not expected to be suitable drugs.”
A decade of fragment-based drug design: strategic advances and lessons learned (2007, unknown): “In addition, the use of structural information in virtual ligand screenin”
Virtual and biomolecular screening converge on a selective agonist for GPR30 (2006, background): “, which has recently become increasingly recognized as a complement to bioactivity screenin”
Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery (2018, methodology): “Machine learning methods can be used for virtual screening of thousands of compounds allowing the utilisation of data from high throughput screening [170,171].”
Can we estimate the accuracy of ADME-Tox predictions? (2006, background): “Incorporation of prediction accuracy in the predicted ADMET properties may significantly improve the quality of compound selection for HTS subset screening [ 58 ], HTS hits triage, hit-to-lead stages of drug development and parallel library design for in-house and outsourced chemistry.”
The Use of Three‐Dimensional Shape and Electrostatic Similarity Searching in the Identification of a Melanin‐Concentrating Hormone Receptor 1 Antagonist (2006, background): “The use of measures of chemical similarity for the in silico identification of novel compounds is a wellestablished means to supplement traditional medicinal chemistry approaches in drug design [1].”
In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling (2007, background): “The process of scoring and ranking molecules in large chemical libraries according to their likelihood of having affinity for a certain target, is generally referred to as virtual screening (Oprea and Matter, 2004).”
Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. (2008, methodology): “Applications and benefits of CADDD have been reviewed and demonstrated in growing number of publications and supported by examples of drugs derived from the in silico approach [9,34–39].”
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017, methodology): “These machine learning methods are increasingly used for virtual screening of compounds which can enable more efficient utilization of high throughput screening (HTS) resources, by enriching the set of compounds screened with compounds that are active 44–47 .”