1.

Record Nr.

UNINA9910456548603321

Titolo

Context and contexts [[electronic resource] ] : parts meet whole? / / edited by Anita Fetzer, Etsuko Oishi

Pubbl/distr/stampa

Amsterdam ; ; Philadelphia, : John Benjamins Pub. Co., 2011

ISBN

1-283-12823-3

9786613128232

90-272-8663-9

Descrizione fisica

vi, 238 p. : ill

Collana

Pragmatics & beyond (P&BNS), , 0922-842X ; ; new series, v. 209

Altri autori (Persone)

FetzerAnita <1958->

OishiEtsuko

Disciplina

401/.41

Soggetti

Context (Linguistics)

Discourse analysis

Social interaction

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Based on papers from the IPrA Conference, which was held in Melbourne in 2009.

Nota di bibliografia

Includes bibliographical references and indexes.



2.

Record Nr.

UNINA9910639987703321

Autore

Udrescu Lucreția

Titolo

In Silico Strategies for Prospective Drug Repositionings

Pubbl/distr/stampa

Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022

ISBN

3-0365-6133-1

Descrizione fisica

1 electronic resource (288 p.)

Soggetti

Medicine

Pharmaceutical industries

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Sommario/riassunto

The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung



cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions.