ZHAO, Guozhen
Enhancing R&D in science-based industry : an optimal stopping model for drug discovery - Exeter, UK : Elsevier, November 2009
Drug discovery is a process of science work full of exploration and complexity. Drug researchers face the dilemma of either working forever to create a perfect drug or delivering a workable drug within an acceptable deadline to both save lives and make a profit. Specifically, in the compound screening and optimization stage, drug researchers must decide whether or not to stop the research and deliver the result for clinical testing. This paper proposes a model of optimal stopping time to help drug researchers make such decisions. It examines the decision strategies drug researchers should take through maximizing their objective function. The numerical examples given here show that this model is widely applicable. The model offers potential merits in terms of its ability to clarify drug researchers decision-making practices and helping managers maneuver through the jungle of science work.
Enhancing R&D in science-based industry : an optimal stopping model for drug discovery - Exeter, UK : Elsevier, November 2009
Drug discovery is a process of science work full of exploration and complexity. Drug researchers face the dilemma of either working forever to create a perfect drug or delivering a workable drug within an acceptable deadline to both save lives and make a profit. Specifically, in the compound screening and optimization stage, drug researchers must decide whether or not to stop the research and deliver the result for clinical testing. This paper proposes a model of optimal stopping time to help drug researchers make such decisions. It examines the decision strategies drug researchers should take through maximizing their objective function. The numerical examples given here show that this model is widely applicable. The model offers potential merits in terms of its ability to clarify drug researchers decision-making practices and helping managers maneuver through the jungle of science work.
