Chemical information theory: Structural aspects

Bonchev, Danail; Trinajstić, Nenad

doi:10.1002/qua.560220845

Cited by 21 publications

(12 citation statements)

References 60 publications

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“…Studies in chemical information theory [30] have demonstrated the promise of entropic perspective in chemistry [31,32,33,34]. We analyze various chemical representations, namely, the SMILES representations, molecular formulas, and our proposed SMILES grammar-based representations from the perspective of Shannon entropy.…”

Section: Information Theory and Chemical Representationsmentioning

confidence: 99%

Retrosynthesis Prediction using Grammar-based Neural Machine Translation: An Information-Theoretic Approach

Mann

Venkatasubramanian²

2021

Preprint

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Retrosynthetic prediction is one of the main challenges in chemical synthesis that requires identifying reaction pathways and precursor molecules for synthesizing a target molecule. This requires a search over the space of plausible chemical reactions that often results in complex, multi-step, branched synthesis trees for even moderately complex organic reactions. Here, we propose an approach that performs single-step retrosynthesis prediction using SMILES grammar-based representations in a neural machine translation framework. Information-theoretic analyses of such grammar-representations reveal that they are both superior and well-suited for machine learning tasks due to their underlying redundancy and high information capacity compared to purely character-based representations. We report the top-1 prediction accuracy of 43.8% (top-5 measure of 61.4%) and syntactic validity of 95.6% (top-5 measure of 91.6%) on a standard reaction dataset. Comparing our model's performance with previous work that used purely character-based SMILES representations demonstrate improved accuracy and reduced grammatically invalid predictions.

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Section: Information Theory and Chemical Representationsmentioning

confidence: 99%

Retrosynthesis Prediction using Grammar-based Neural Machine Translation: An Information-Theoretic Approach

Mann

Venkatasubramanian²

2021

Preprint

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“…In the mid 1940s, Wiener (1948), was one of the first authors to describe information as "information is neither matter nor energy" [5]. Later on, several works have emphasized the role of information in diverse disciplines, such as molecular biology, economics, linguistics and chemical kinetics [6].…”

Section: The Basic Principles Of Informationmentioning

confidence: 99%

Information, Thermodynamics and Life: A Narrative Review

et al. 2021

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Information is probably one of the most difficult physical quantities to comprehend. This applies not only to the very definition of information, but also to the physical entity of information, meaning how can it be quantified and measured. In recent years, information theory and its function in systems has been an intense field of study, due to the large increase of available information technology, where the notion of bit dominated the information discipline. Information theory also expanded from the “simple” “bit” to the quantal “qubit”, which added more variables for consideration. One of the main applications of information theory could be considered the field of “autonomy”, which is the main characteristic of living organisms in nature since they all have self-sustainability, motion and self-protection. These traits, along with the ability to be aware of existence, make it difficult and complex to simulate in artificial constructs. There are many approaches to the concept of simulating autonomous behavior, yet there is no conclusive approach to a definite solution to this problem. Recent experimental results have shown that the interaction between machines and neural cells is possible and it consists of a significant tool for the study of complex systems. The present work tries to review the question on the interactions between information and life. It attempts to build a connection between information and thermodynamics in terms of energy consumption and work production, as well as present some possible applications of these physical quantities.

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“…Using: summed over all vertices of a graph. [25][26][27][28][29][30][31][32] For a review of these and other alternative approaches to distance-based molecular information indices consult Ref. [25][26][27][28][29][30][31][32] For a review of these and other alternative approaches to distance-based molecular information indices consult Ref.…”

Section: Isocentrality and Isocentral Graphsmentioning

confidence: 99%

“…Earlier alternative information indices based on distances in a graph were considered by Rashevsky, [21,22] Trucco, [23,24] and by Bonchev, Trinajstić, and coworkers. [25][26][27][28][29][30][31][32] For a review of these and other alternative approaches to distance-based molecular information indices consult Ref. [20].…”

Section: Isocentrality and Isocentral Graphsmentioning

confidence: 99%

On the centrality of vertices of molecular graphs

et al. 2013

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For acyclic systems the center of a graph has been known to be either a single vertex of two adjacent vertices, that is, an edge. It has not been quite clear how to extend the concept of graph center to polycyclic systems. Several approaches to the graph center of molecular graphs of polycyclic graphs have been proposed in the literature. In most cases alternative approaches, however, while being apparently equally plausible, gave the same results for many molecules, but occasionally they differ in their characterization of molecular center. In order to reduce the number of vertices that would qualify as forming the center of the graph, a hierarchy of rules have been considered in the search for graph centers. We reconsidered the problem of "the center of a graph" by using a novel concept of graph theory, the vertex "weights," defined by counting the number of pairs of vertices at the same distance from the vertex considered. This approach gives often the same results for graph centers of acyclic graphs as the standard definition of graph center based on vertex eccentricities. However, in some cases when two nonequivalent vertices have been found as graph center, the novel approach can discriminate between the two. The same approach applies to cyclic graphs without additional rules to locate the vertex or vertices forming the center of polycyclic graphs, vertices referred to as central vertices of a graph. In addition, the novel vertex "weights," in the case of acyclic, cyclic, and polycyclic graphs can be interpreted as vertex centralities, a measure for how close or distant vertices are from the center or central vertices of the graph. Besides illustrating the centralities of a number of smaller polycyclic graphs, we also report on several acyclic graphs showing the same centrality values of their vertices.

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Chemical information theory: Structural aspects

Cited by 21 publications

References 60 publications

Retrosynthesis Prediction using Grammar-based Neural Machine Translation: An Information-Theoretic Approach

Retrosynthesis Prediction using Grammar-based Neural Machine Translation: An Information-Theoretic Approach

Information, Thermodynamics and Life: A Narrative Review

On the centrality of vertices of molecular graphs

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