Molecules in Silico:  A Graph Description of Chemical Reactions

Kerber, Adalbert; Laue, Reinhard; Meringer, Markus; Rücker, Christoph

doi:10.1021/ci600470q

Cited by 16 publications

(6 citation statements)

References 25 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…if no more new reaction products are generated, or a certain number of iterations is reached, structure generation terminates. Reaction-based structure generation has been implemented using reaction schemes [ 41 ], which describe generic chemical reactions by a reaction centre graph and changes occurring during the reaction, closely related to the Dugundji–Ugi model [ 42 ] and the approach of Temkin et al [ 43 ]. Graph grammars, as introduced in [ 44 ], have already been applied to problems in origins of life research [ 45 – 47 ].…”

Section: Methodsmentioning

confidence: 99%

Exploring astrobiology using in silico molecular structure generation

Meringer

Cleaves

2017

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

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’.

show abstract

Section: Methodsmentioning

confidence: 99%

Exploring astrobiology using in silico molecular structure generation

Meringer

Cleaves

2017

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To generate the complete ribosides, the final output sd file then had the chlorine atom replaced with AUGC or T via an N 9 linkage for the purines or an N 1 linkage for the pyrimidines. For this, MOLGEN–COMB's ability to virtually execute user-defined reactions on multiple reactants was used (Gugisch et al , 2000 ; Kerber et al , 2007 ).…”

Section: Methodsmentioning

confidence: 99%

227 Views of RNA: Is RNA Unique in Its Chemical Isomer Space?

2015

Self Cite

View full text Add to dashboard Cite

Ribonucleic acid (RNA) is one of the two nucleic acids used by extant biochemistry and plays a central role as the intermediary carrier of genetic information in transcription and translation. If RNA was involved in the origin of life, it should have a facile prebiotic synthesis. A wide variety of such syntheses have been explored. However, to date no one-pot reaction has been shown capable of yielding RNA monomers from likely prebiotically abundant starting materials, though this does not rule out the possibility that simpler, more easily prebiotically accessible nucleic acids may have preceded RNA. Given structural constraints, such as the ability to form complementary base pairs and a linear covalent polymer, a variety of structural isomers of RNA could potentially function as genetic platforms. By using structure-generation software, all the potential structural isomers of the ribosides (BC5H9O4, where B is nucleobase), as well as a set of simpler minimal analogues derived from them, that can potentially serve as monomeric building blocks of nucleic acid–like molecules are enumerated. Molecules are selected based on their likely stability under biochemically relevant conditions (e.g., moderate pH and temperature) and the presence of at least two functional groups allowing the monomers to be incorporated into linear polymers. The resulting structures are then evaluated by using molecular descriptors typically applied in quantitative structure–property relationship (QSPR) studies and predicted physicochemical properties. Several databases have been queried to determine whether any of the computed isomers had been synthesized previously. Very few of the molecules that emerge from this structure set have been previously described. We conclude that ribonucleosides may have competed with a multitude of alternative structures whose potential proto-biochemical roles and abiotic syntheses remain to be explored. Key Words: Evolution—Chemical evolution—Exobiology—Prebiotic chemistry—RNA world. Astrobiology 15, 538–558.

show abstract

“…al., 33 and Kerber et. al., 34 but these all encapsulate overall transformations, or general graph rearrangements, and none are analogous to such an ubiquitous chemical idea as “arrow-pushing.”…”

Section: Molecular Orbital Reaction Modelmentioning

confidence: 99%

Learning to Predict Chemical Reactions

Kayala

Azencott

Chen

et al. 2011

J. Chem. Inf. Model.

181

178

View full text Add to dashboard Cite

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal (http://cdb.ics.uci.edu) under the Toolkits section.

show abstract

Molecules in Silico: A Graph Description of Chemical Reactions

Cited by 16 publications

References 25 publications

Exploring astrobiology using in silico molecular structure generation

Exploring astrobiology using in silico molecular structure generation

227 Views of RNA: Is RNA Unique in Its Chemical Isomer Space?

Learning to Predict Chemical Reactions

Contact Info

Product

Resources

About