TY - JOUR
T1 - Novel opportunities for computational biology and sociology in drug discovery
AU - Yao, Lixia
AU - Evans, James A.
AU - Rzhetsky, Andrey
N1 - Funding Information:
We are grateful to Rajeev Aurora, Carolyn Cho, Murat Cokol, Preston Hensley, Ivan Iossifov, Aaron J. Mackey, and Nathan Siemers for insightful discussions and comments on earlier versions of the manuscript. This work was supported by the U.S. National Institutes of Health (GM61372 and U54 CA121852-01A1) and the National Science Foundation (0242971).
PY - 2010/4
Y1 - 2010/4
N2 - 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.
AB - 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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U2 - 10.1016/j.tibtech.2010.01.004
DO - 10.1016/j.tibtech.2010.01.004
M3 - Comment/debate
C2 - 20349528
AN - SCOPUS:77949655881
SN - 0167-7799
VL - 28
SP - 161
EP - 170
JO - Trends in Biotechnology
JF - Trends in Biotechnology
IS - 4
ER -