Abstract
A mixed-integer nonlinear program (MINLP) algorithm to optimize catalyst turnover number (TON) and product yield by simultaneously modulating discrete variables—catalyst types—and continuous variables—temperature, residence time, and catalyst loading—was implemented and validated.
Highlights
Drug research and development is a complex process that is estimated to require an average of more than 13 years and $1.8 billion to bring a single product to market.[1]
Updating the response surface model and fathoming branches of the tree after each batch of experiments allows us to focus experimental effort on conditions that are more likely to be close to the global optimum
We discuss the simulation and experimental results of the algorithm introduced in this paper (MINLP 2) and compare them to results obtained with the previous algorithm (MINLP 1) published by Reizman et al.[34]
Summary
Drug research and development is a complex process that is estimated to require an average of more than 13 years and $1.8 billion to bring a single product to market.[1]. The optimization of reactions conditions to improve target values such as product yield, throughput, or chemical costs is a necessity to do so economically. Nature of selecting reaction conditions presents a challenge to the secondary goal of minimizing the material usage and experimental time. Murray et al.[3] estimate that when considering multiple discrete and continuous variables, the testing of millions of combinations might be required to exhaustively screen one typical transition metal catalyzed reaction. Such an enormous effort is rarely necessary because this “curse of dimensionality” can be mitigated by the use of optimization algorithms. Even without encoding prior chemical knowledge, chemical synthesis can be amenable to standard optimization techniques because response surfaces are often relatively smooth and well-behaved.[4]
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