New elements in the representation of the logical structure of chemistry by qualitative mathematical models and corresponding data structures

Ugi, Ivar; Stein, Natalie; Knauer, Michael; Gruber, Bernhard; Bley, K.; Weidinger, Rupert

doi:10.1007/bfb0111463

Cited by 23 publications

(19 citation statements)

References 26 publications

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“…[36][37][38][39] Our definition of the shortest path metric on the set of bond breaking and bond making events is similar to the reaction distance definition by Kvasnička and co-workers [40][41][42][43] and the chemical distance defined on the set of bond-electron (BE) matrices of Dugundji and Ugi. 31,44,45 More broadly, graph-theoretical concepts have been successfully used for developing structure descriptors [46][47][48] and structure generation. [49][50][51][52] The ideas of metrics 10 and partial orderings 15,16 have been discussed in the context of topological descriptors of molecules and chemical reactions.…”

Section: Introductionmentioning

confidence: 99%

Reaction Networks and the Metric Structure of Chemical Space(s)

Rappoport

2019

J. Phys. Chem. A

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In this paper we develop a formal definition of chemical space as a discrete metric space of molecules and analyze its properties. To this end, we utilize the shortest path metric on reaction networks to define a distance function between molecules of the same stoichiometry (number of atoms). The distance between molecules with different stoichiometries is formalized by making use of the partial ordering of stoichiometries with respect to inclusion. Calculations of fractal dimension on metric spaces for individual stoichiometries show that they have low intrinsic dimensionality, about an order of magnitude less than the dimension of the underlying reactive potential energy surface. Our findings suggest that efficient search strategies on chemical space can be designed that take advantage of its metric structure.

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Section: Introductionmentioning

confidence: 99%

Reaction Networks and the Metric Structure of Chemical Space(s)

Rappoport

2019

J. Phys. Chem. A

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“…In the symbolically extended BE (sXBE) matrices [118][119][120][121], however, delocalized electron systems are encoded using special bond types such as pisys (e.g., benzene) or edsys (for electron deficient systems such as boranes). Therefore, these representations allow for a better representation of the true multicenter bonding nature of some systems such as diborane or ferrocene (Figure 10A-B).…”

Section: Separation Of σ-And π-Electron Systemsmentioning

confidence: 99%

SELFIES and the future of molecular string representations

Krenn¹,

Ai²,

Barthel³

et al. 2022

Preprint

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Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for

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“…The result is a new λ-term. Related models are based on a wide variety of different computational paradigms from strings and matrices to Turing machines and graphs [8][9][10][11][12][13][14], see also the reviews [15,16]. The abstract computational models are very useful for understanding algebraic properties of reaction systems; the notion of a selfmaintaining set and the development of a theory of Chemical Organizations [17] emphasizes the success of such approaches.…”

Section: The Chemical Model Universementioning

confidence: 99%

Evolution of metabolic networks: a computational frame-work

et al. 2010

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Background: The metabolic architectures of extant organisms share many key pathways such as the citric acid cycle, glycolysis, or the biosynthesis of most amino acids. Several competing hypotheses for the evolutionary mechanisms that shape metabolic networks have been discussed in the literature, each of which finds support from comparative analysis of extant genomes. Alternatively, the principles of metabolic evolution can be studied by direct computer simulation. This requires, however, an explicit implementation of all pertinent components: a universe of chemical reaction upon which the metabolism is built, an explicit representation of the enzymes that implement the metabolism, of a genetic system that encodes these enzymes, and of a fitness function that can be selected for. Results: We describe here a simulation environment that implements all these components in a simplified ways so that large-scale evolutionary studies are feasible. We employ an artificial chemistry that views chemical reactions as graph rewriting operations and utilizes a toy-version of quantum chemistry to derive thermodynamic parameters. Minimalist organisms with simple string-encoded genomes produce model ribozymes whose catalytic activity is determined by an ad hoc mapping between their secondary structure and the transition state graphs that they stabilize. Fitness is computed utilizing the ideas of metabolic flux analysis. We present an implementation of the complete system and first simulation results. Conclusions: The simulation system presented here allows coherent investigations into the evolutionary mechanisms of the first steps of metabolic evolution using a self-consistent toy universe.

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New elements in the representation of the logical structure of chemistry by qualitative mathematical models and corresponding data structures

Cited by 23 publications

References 26 publications

Reaction Networks and the Metric Structure of Chemical Space(s)

Reaction Networks and the Metric Structure of Chemical Space(s)

SELFIES and the future of molecular string representations

Evolution of metabolic networks: a computational frame-work

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