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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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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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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.
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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

Accounting for receptor flexibility and enhanced sampling methods in computer aided drug design

TL;DR: Modifications to standard rigid receptor docking algorithms are investigated and the combination of free energy calculations and enhanced sampling techniques are explored, which may help improve the efficiency of drug discovery and development.
Book ChapterDOI

Ab Initio Protein Structure Prediction

TL;DR: Ab initio modeling as mentioned in this paper is a template-based modeling procedure that can help answer the questions of how and why a protein adopts its specific structure, and it can also help us understand the physicochemical principle of how proteins fold in nature.
Journal ArticleDOI

Synthesis and structure-activity relationships of potent thrombin inhibitors: piperazides of 3-amidinophenylalanine.

TL;DR: Some of the newly synthesized compounds are potent inhibitors of thrombin and offer an approach to study structure-function relationships for inhibition of thROMbin and related enzymes and for the improvement of their pharmacokinetic properties.
Journal ArticleDOI

RosettaEPR: an integrated tool for protein structure determination from sparse EPR data.

TL;DR: RosettaEPR is introduced, which has been designed to improve de novo high-resolution protein structure prediction using sparse SDSL-EPR distance data and yields a 1.7Å model of T4-lysozyme, indicating that atomic detail models can be achieved by combining sparse EPR data with Rosetta.
Journal ArticleDOI

Predicting the Solubility of the Anti-Cancer Agent Docetaxel in Small Molecule Excipients using Computational Methods

TL;DR: The in silico MD model proved to be a reliable tool for selecting suitable excipients for the solubilization of docetaxel and correlated well with experimental studies.
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