Literature DB >> 19674801

Novel opportunities for computational biology and sociology in drug discovery.

Lixia Yao1, James A Evans, Andrey Rzhetsky.   

Abstract

Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies.

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Year:  2009        PMID: 19674801      PMCID: PMC2761076          DOI: 10.1016/j.tibtech.2009.06.003

Source DB:  PubMed          Journal:  Trends Biotechnol        ISSN: 0167-7799            Impact factor:   19.536


  47 in total

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Review 7.  Lovastatin and beyond: the history of the HMG-CoA reductase inhibitors.

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Review 9.  Novel statistical tools for monitoring the safety of marketed drugs.

Authors:  J S Almenoff; E N Pattishall; T G Gibbs; W DuMouchel; S J W Evans; N Yuen
Journal:  Clin Pharmacol Ther       Date:  2007-05-30       Impact factor: 6.875

10.  Harnessing naturally randomized transcription to infer regulatory relationships among genes.

Authors:  Lin S Chen; Frank Emmert-Streib; John D Storey
Journal:  Genome Biol       Date:  2007       Impact factor: 13.583

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  2 in total

Review 1.  Combining in vitro and in silico Approaches to Find New Candidate Drugs Targeting the Pathological Proteins Related to the Alzheimer's Disease.

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Journal:  Curr Neuropharmacol       Date:  2018       Impact factor: 7.363

2.  Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.

Authors:  Joel Markus Vaz; S Balaji
Journal:  Mol Divers       Date:  2021-05-24       Impact factor: 3.364

  2 in total

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