Structure Enumeration and Sampling

Markus, Markus

doi:10.1201/9781420082999-c8

Cited by 10 publications

(15 citation statements)

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“…Many elements of this work have been previously discussed in the chemistry literature. Reaction networks play a central role in kinetic modeling of complex chemical reactions − and origins of life research. − The analysis of their graph structures has provided insights into mechanistic complexity and catalysis. − Independently, automatic synthesis prediction − spurred intensive research in compact representations of molecules and reactions and utilized graph theory to explore the network of synthetic chemistry. − 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 − and the chemical distance defined on the set of bond–electron (BE) matrices of Dugundji and Ugi. ,, More broadly, graph-theoretical concepts have been successfully used for developing structure descriptors − and structure generation. − The ideas of metrics and partial orderings , have been discussed in the context of topological descriptors of molecules and chemical reactions. Our construction of chemical space benefits from the insights of many of these seminal works.…”

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 this method, the corresponding connectivity isomers are generated completely and non-redundantly. The algorithmic principle applied here is called orderly generation [ 12 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Exploring astrobiology using in silico molecular structure generation

Meringer

Cleaves

2017

Phil. Trans. R. Soc. A.

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The origin of life is typically understood as a transition from inanimate or disorganized matter to self-organized, ‘animate’ matter. This transition probably took place largely in the context of organic compounds, and most approaches, to date, have focused on using the organic chemical composition of modern organisms as the main guide for understanding this process. However, it has gradually come to be appreciated that biochemistry, as we know it, occupies a minute volume of the possible organic ‘chemical space’. As the majority of abiotic syntheses appear to make a large set of compounds not found in biochemistry, as well as an incomplete subset of those that are, it is possible that life began with a significantly different set of components. Chemical graph-based structure generation methods allow for exhaustive in silico enumeration of different compound types and different types of ‘chemical spaces’ beyond those used by biochemistry, which can be explored to help understand the types of compounds biology uses, as well as to understand the nature of abiotic synthesis, and potentially design novel types of living systems.This article is part of the themed issue ‘Reconceptualizing the origins of life’.

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“…On one hand, the enumeration of isomers (or, in the language of graph theory, enumeration of sets of graphs with the same “degree sequence” , ) is a tool of central importance to structural elucidation . On the other hand, the enumeration of small-molecule chemical spaces has been an exciting approach in drug design and discovery. − …”

Section: Introductionmentioning

confidence: 99%

“…The advantage is that they are very fast to runhence, these were used in GDB when the size of the chemical space grew quickly. MOLGEN can even prune during enumeration by stopping the orderly generation , process when a badlist substructure appears. Force field/simulation (3D)-based methods are less likely to reject genuine moleculesas calculations are based on first principles. ,, In the majority of cases, when the topology of real molecules is used, we would expect common conformer generators (see Ebejer et al) to find reasonable conformers.…”

Section: Introductionmentioning

confidence: 99%

A Novel Automated Screening Method for Combinatorially Generated Small Molecules

Sterling

Hein

2021

J. Chem. Inf. Model.

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A main challenge in the enumeration of smallmolecule chemical spaces for drug design is to quickly and accurately differentiate between possible and impossible molecules. Current approaches for screening enumerated molecules (e.g., 2D heuristics and 3D force fields) have not been able to achieve a balance between accuracy and speed. We have developed a new automated approach for fast and high-quality screening of small molecules, with the following steps: (1) for each molecule in the set, an ensemble of 2D descriptors as feature encoding is computed; (2) on a random small subset, classification (feasible/ infeasible) targets via a 3D-based approach are generated; (3) a classification dataset with the computed features and targets is formed and a machine learning model for predicting the 3D approach's decisions is trained; and (4) the trained model is used to screen the remainder of the enumerated set. Our approach is ≈8× (7.96× to 8.84×) faster than screening via 3D simulations without significantly sacrificing accuracy; while compared to 2Dbased pruning rules, this approach is more accurate, with better coverage of known feasible molecules. Once the topological features and 3D conformer evaluation methods are established, the process can be fully automated, without any additional chemistry expertise.

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Structure Enumeration and Sampling

Cited by 10 publications

References 0 publications

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

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

Exploring astrobiology using in silico molecular structure generation

A Novel Automated Screening Method for Combinatorially Generated Small Molecules

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