Decisions in drug development are made on the basis of determinations of cause and effect from experimental observations that span drug development phases. Despite advances in our powers of observation, the ability to determine compound mechanisms from large-scale multi-omic technologies continues to be a major bottleneck. This can only be overcome by utilizing computational learning methods that identify from compound data the circuits and connections between drug-affected molecular constituents and physiological observables. The marriage of multi-omics technologies with network inference approaches will provide missing insights needed to improve drug development success rates.
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| http://dx.doi.org/10.1016/j.drudis.2007.10.001 | DOI Listing |
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