Network search algorithms and scoring functions for advanced‐level computerized synthesis planning

Grzybowski, Bartosz A.; Badowski, Tomasz; Molga, Karol; Szymkuć, Sara

doi:10.1002/wcms.1630

Cited by 13 publications

(13 citation statements)

References 66 publications

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“…Determining the most probable sequence of reactions between two compounds is a shortest path problem. Extracting such paths in terms of a likelihood for reaction mechanisms and synthesis routes has been accomplished based on depth-first algorithms. − A drawback of these approaches is that they are specifically tailored solutions for a specially constructed and pruned CRN. For example, the stoichiometric requirements of a reaction are not considered or circumvented by construction.…”

Section: Introductionmentioning

confidence: 99%

Pathfinder─Navigating and Analyzing Chemical Reaction Networks with an Efficient Graph-Based Approach

Türtscher

Reiher

2022

J. Chem. Inf. Model.

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While the field of first-principles explorations into chemical reaction space has been continuously growing, the development of strategies for analyzing resulting chemical reaction networks (CRNs) is lagging behind. A CRN consists of compounds linked by reactions. Analyzing how these compounds are transformed into one another based on kinetic modeling is a nontrivial task. Here, we present the graph-optimization-driven algorithm and program PATHFINDER to allow for such an analysis of a CRN. The CRN for this work has been obtained with our opensource CHEMOTON reaction network exploration software. CHEMO-TON probes reactive combinations of compounds for elementary steps and sorts them into reactions. By encoding these reactions of the CRN as a graph consisting of compound and reaction vertices and adding information about activation barriers as well as required reagents to the edges of the graph yields a complete graphtheoretical representation of the CRN. Since the probabilities of the formation of compounds depend on the starting conditions, the consumption of any compound during a reaction must be accounted for to reflect the availability of reagents. To account for this, we introduce compound costs to reflect compound availability. Simultaneously, the determined compound costs rank the compounds in the CRN in terms of their probability to be formed. This ranking then allows us to probe easily accessible compounds in the CRN first for further explorations into yet unexplored terrain. We first illustrate the working principle on an abstract small CRN. Afterward, PATHFINDER is demonstrated in the example of the disproportionation of iodine with water and the comproportionation of iodic acid and hydrogen iodide. Both processes are analyzed within the same CRN, which we construct with our autonomous firstprinciples CRN exploration software CHEMOTON [

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Section: Introductionmentioning

confidence: 99%

Pathfinder─Navigating and Analyzing Chemical Reaction Networks with an Efficient Graph-Based Approach

Türtscher

Reiher

2022

J. Chem. Inf. Model.

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“…While a small set of couplings under a limited range of conditions can be useful for specific applications, it is a much greater challenge to build a self-driving lab that can make molecules of arbitrary structure. However, this outstanding challenge is too complex to be addressed here and continues to see significant ongoing developments that lead to expanded capabilities. ,,,− In summary, to run smoothly, a self-driving lab must be controlled by robust algorithms and code. Human cognitive capabilities can usually handle such tasks with ease; however, their automation can be very difficult.…”

Section: Challenges and Lessons Learnedmentioning

confidence: 99%

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

et al. 2022

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Conspectus We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning models can be trained on large data sets from the literature or databases, but their performance can often be hampered by a lack of negative results or metadata. In contrast, data generated by self-driving laboratories can be information-rich, containing precise details of the experimental conditions and metadata. Consequently, much larger amounts of high-quality data are gathered in self-driving laboratories. When placed in open repositories, this data can be used by the research community to reproduce experiments, for more in-depth analysis, or as the basis for further investigation. Accordingly, high-quality open data sets will increase the accessibility and reproducibility of science, which is sorely needed. In this Account, we describe our efforts to build a self-driving lab for the development of a new class of materials: organic semiconductor lasers (OSLs). Since they have only recently been demonstrated, little is known about the molecular and material design rules for thin-film, electrically-pumped OSL devices as compared to other technologies such as organic light-emitting diodes or organic photovoltaics. To realize high-performing OSL materials, we are developing a flexible system for automated synthesis via iterative Suzuki–Miyaura cross-coupling reactions. This automated synthesis platform is directly coupled to the analysis and purification capabilities. Subsequently, the molecules of interest can be transferred to an optical characterization setup. We are currently limited to optical measurements of the OSL molecules in solution. However, material properties are ultimately most important in the solid state (e.g., as a thin-film device). To that end and for a different scientific goal, we are developing a self-driving lab for inorganic thin-film materials focused on the oxygen evolution reaction. While the future of self-driving laboratories is very promising, numerous challenges still need to be overcome. These challenges can be split into cognition and motor function. Generally, the cognitive challenges are related to optimization with constraints or unexpected outcomes for which general algorithmic solutions have yet to be developed. A more practical challenge that could be resolved in the near future is that of software control and integration because few instrument manufacturers d...

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“…Numerous recent review and perspective articles have extensively explored the role of data science, ML and AI in various domains of experimental chemistry, including general chemistry, 1 synthetic chemistry and chemical reactions, [2][3][4][5] as well as theoretical topics such as chemical compound space exploration 6 and force-eld development. 7,8 Additionally, recent reviews have addressed the application of autonomous research systems in materials science, [9][10][11][12][13][14][15][16] organic chemistry, [17][18][19] inorganic chemistry, 20 porous materials, 21 nanoscience, 22,23 drug formulation 24,25 and biomaterials. 26 Reviews also exist on the topic of self-driving laboratories 27,28 and their low-cost incarnations.…”

Section: Introductionmentioning

confidence: 99%

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Network search algorithms and scoring functions for advanced‐level computerized synthesis planning

Cited by 13 publications

References 66 publications

Pathfinder─Navigating and Analyzing Chemical Reaction Networks with an Efficient Graph-Based Approach

Pathfinder─Navigating and Analyzing Chemical Reaction Networks with an Efficient Graph-Based Approach

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

Accelerated chemical science with AI

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