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Open AccessJournal ArticleDOI

Computational methods in drug discovery.

TLDR
An overview of computational methods used in different facets of drug discovery and highlight some of the recent successes is presented, both structure-based and ligand-based drug discovery methods are discussed.
Abstract
The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

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Drug Discovery Today

TL;DR: A wide range of new lead finding and lead optimization opportunities result from novel screening methods by NMR, which are the topic of this review article.
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Kinase inhibitors: the road ahead

TL;DR: An overview of the novel targets, biological processes and disease areas that kinase-targeting small molecules are being developed against, highlight the associated challenges and assess the strategies and technologies that are enabling efficient generation of highly optimized kinase inhibitors are provided.
Journal ArticleDOI

Structure-Based Virtual Screening: From Classical to Artificial Intelligence.

TL;DR: An overview of the challenges involved in the use of CADD to performSBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process are presented.
References
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Journal ArticleDOI

Machine-learning approaches in drug discovery: methods and applications.

TL;DR: This work focuses on machine-learning techniques within the context of ligand-based VS (LBVS), providing a detailed view of the current state of the art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances.
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Efficient drug lead discovery and optimization

TL;DR: Striking success has now been achieved for computer-aided drug lead generation and optimization for HIV reverse transcriptase inhibitors and inhibitors of the binding of the proinflammatory cytokine MIF to its receptor CD74.
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Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification

TL;DR: Although SVM outperformed the ANN classifiers with regard to overall prediction accuracy, both methods were shown to complement each other, as the sets of true positives, false positives, true negatives, and false negatives produced by the two classifiers were not identical.
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Enhanced sampling techniques in molecular dynamics simulations of biological systems

TL;DR: An overview over theses sampling methods is presented in an attempt to shed light on which should be selected depending on the type of system property studied, and whether metadynamics and replica-exchange molecular dynamics are the most adopted sampling methods to study biomolecular dynamics.
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Machine learning and its applications to biology.

TL;DR: This tutorial discusses the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data in the field of supervised learning in R, the open source data analysis and visualization language.
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