Why is chemical synthesis and property optimization easier than expected?

Moore, Katharine W.; Pechen, Alexander; Xiao, Feng; Dominy, Jason; Beltrani, Vincent; Rabitz, Herschel

doi:10.1039/c1cp20353c

Cited by 66 publications

(107 citation statements)

References 183 publications

Supporting

Mentioning

104

Contrasting

Unclassified

Order By: Relevance

“…In principle, an optimal solution may be found by starting anywhere on the control landscape and employing an efficient search algorithm. This prediction is consistent with the findings that chemical objectives can often be optimized in significantly fewer experiments than implied by the "size" of typical search spaces [38]. (b) Observed trapping indicates constrained variables: The fundamental existence of trap-free landscapes provides a framework to understand trapping behavior when it occurs.…”

Section: Predictions Of Optichem Theorysupporting

confidence: 74%

“…Energy landscapes constitute a specific sub-class drawn from the general family of control landscapes in chemistry because the variables are often highly constrained, for instance, the only significant variable freedom for protein folding is torsional motion. While energy landscapes typically have a rugged topology [58], recent studies [37,38] demonstrate that control landscapes in chemistry possess a "regular" trap-free topology, upon satisfaction of Assumptions (1), (2) and (3).…”

Section: Introductionmentioning

confidence: 98%

“…Importantly, the landscape analysis underlying OptiChem may be carried out with minimal assumptions about the specific nature of the control variables employed. OptiChem theory makes general predictions about the behavior of synthesis and property optimizations that are widely confirmed in laboratory studies [38], and also leads to specific algorithmic suggestions on how to exploit the landscape topology to significantly accelerate laboratory optimization efforts and provide new tools for chemical property prediction.…”

Section: Introductionmentioning

confidence: 99%

“…The topological analysis of the resulting control landscape and its practical implications have recently been termed "OptiChem" theory [37,38]. The presence of a trap-free control landscape provides an organizing principle for explaining why synthesis and property discovery is far easier than the apparent complexity of such problems would suggest.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the application of genetic algorithms (GA) [20] and other optimal search procedures shows that optimization of both synthesis yield and properties typically may be achieved by carefully screening a small number of potential combinations of variables, even for nominally highly complex systems. An extensive review of practical synthesis and property optimizations beating the curse of dimensionality is given in [37,38]. The origin of these mounting successes is the key topic addressed in this section.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fundamental Principles of Control Landscapes with Applications to Quantum Mechanics, Chemistry and Evolution

Rabitz

et al. 2014

Recent Advances in the Theory and Application of Fitness Landscapes

Self Cite

View full text Add to dashboard Cite

Abstract. The concept of a landscape or response surface naturally arises in applications widely ranging over the sciences, engineering and other disciplines. A landscape is the desired output as a function of a set of input variables, often of very high dimension. The relationship between the features of a landscape and the input variables is usually unknown a priori and often thought to be highly complex due to the anticipated intricate interactions involved. This chapter reviews recent developments in the analysis of landscape topology with the input variables considered as controls. Taking a control perspective allows for the specification of particular assumptions whose satisfaction permits a general analysis of the landscape topology. Satisfaction of these conditions leads to the conclusion that control landscapes should be devoid of suboptimal critical point traps, thereby permitting ready excursions without hindrance to the highest values of the landscape. These principles are set out in a general framework and then specifically illustrated for applications

show abstract

Section: Predictions Of Optichem Theorysupporting

confidence: 74%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fundamental Principles of Control Landscapes with Applications to Quantum Mechanics, Chemistry and Evolution

Rabitz

et al. 2014

Recent Advances in the Theory and Application of Fitness Landscapes

Self Cite

View full text Add to dashboard Cite

show abstract

Enhancing the Efficiency of Directed Evolution in Focused Enzyme Libraries by the Adaptive Substituent Reordering Algorithm

Xiao

Sanchis

Reetz

et al. 2012

Chemistry A European J

View full text Add to dashboard Cite

Directed evolution is a broadly successful strategy for protein engineering in the quest to enhance the stereoselectivity, activity, and thermostability of enzymes. To increase the efficiency of directed evolution based on iterative saturation mutagenesis, the adaptive substituent reordering algorithm (ASRA) is introduced here as an alternative to traditional quantitative structure-activity relationship (QSAR) methods for identifying potential protein mutants with desired properties from minimal sampling of focused libraries. The operation of ASRA depends on identifying the underlying regularity of the protein property landscape, allowing it to make predictions without explicit knowledge of the structure-property relationships. In a proof-of-principle study, ASRA identified all or most of the best enantioselective mutants among the synthesized epoxide hydrolase from Aspergillus niger, in the absence of peptide seeds with high E-values. ASRA even revealed a laboratory error from irregularities of the reordered E-value landscape alone.

show abstract

Computer‐Assisted Synthesis Optimisation of Inorganic–Organic Hybrid Compounds Using the Local Optimisation Algorithm BOBYQA

Schilling¹,

Niekiel²,

Stock³

et al. 2014

ChemPlusChem

View full text Add to dashboard Cite

The local, deterministic optimisation algorithm BOBYQA (bound optimisation by quadratic approximation) was applied for the computer‐assisted optimisation of inorganic–organic hybrid compounds. Four proof‐of‐concept studies were performed: 1) increasing the crystallinity and 2) inhibiting the crystallisation of [Ca(H2O)2(H2PMBC)2] (H2PMBC−=−HO3P‐CH2‐C4H6‐COOH), 3) increasing the crystal size of [Bi(H2O)(BTC)] (BTC3−=benzene‐1,3,5‐tricarboxylate) and 4) tuning the particle sizes of [Al(OH)(CDC)]⋅x H2O (CDC2−=trans‐1,4‐cyclohexane dicarboxylate) to a desired value. The measurable quantities of crystallinity, crystal size and hydrodynamic diameter were used as the quality criteria for the optimisation, and the parameters of reaction time and temperature, molar ratios and overall concentration of the starting materials as well as the stirring rate were varied. The crystallinity of [Ca(H2O)2(H2PMBC)2] was increased in three optimisation steps by approximately 14 %, which was accompanied by an increase in crystal size by a factor of approximately 40. These crystals were suitable for structure determination from single‐crystal X‐ray diffraction data. The crystallisation of the same compound could be completely inhibited and clear solutions were obtained. The average crystal size of [Bi(H2O)(BTC)] was increased from (22.2±4.5) to (34.8±9.5) μm and the upper limit increased from 30.0 to 57.7 μm over the course of the optimisation. For the application in Bragg stacks, the variation of particle sizes of [Al(OH)(CDC)]⋅x H2O was studied. Although we aimed at a decrease to 100 nm, a lower limit of 460 nm and polydispersity index of 0.03 were accomplished. The convergence of the algorithm indicates that the optimisation progress is close to completion and the found value for the particle size is close to the lower limit in the system of the chosen parameters. These proofs‐of‐concept studies demonstrate the potential of optimisation algorithms like BOBYQA in synthesis optimisation experiments. At the same time, the convergence behaviour of the algorithm gives an indication of the progress of an optimisation.

show abstract

Why is chemical synthesis and property optimization easier than expected?

Cited by 66 publications

References 183 publications

Fundamental Principles of Control Landscapes with Applications to Quantum Mechanics, Chemistry and Evolution

Fundamental Principles of Control Landscapes with Applications to Quantum Mechanics, Chemistry and Evolution

Enhancing the Efficiency of Directed Evolution in Focused Enzyme Libraries by the Adaptive Substituent Reordering Algorithm

Computer‐Assisted Synthesis Optimisation of Inorganic–Organic Hybrid Compounds Using the Local Optimisation Algorithm BOBYQA

Contact Info

Product

Resources

About