Universal Structure Conversion Method for Organic Molecules: From Atomic Connectivity to Three‐Dimensional Geometry

Kim, Yeonjoon; Kim, Woo Youn

doi:10.1002/bkcs.10334

Cited by 56 publications

(55 citation statements)

References 39 publications

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“…In recent years, kinetic modeling has changed from being a manual reaction network assembling task performed by experienced kineticists to one in which software packages are used to generate comprehensive reaction mechanisms automatically based on user‐defined reaction rules. Such software packages need to keep track of the formation and consumption of all participating species, including reactive intermediates.…”

Section: Introductionmentioning

confidence: 99%

Group additive modeling of substituent effects in monocyclic aromatic hydrocarbon radicals

et al. 2016

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The thermodynamic properties of the unsubstituted and substituted phenyl, phenoxy, anisyl, benzoyl, styryl and benzyl radicals with six substituents (hydroxy, methoxy, formyl, vinyl, methyl, and ethyl) are calculated with the bond additivity corrected (BAC) post‐Hartree‐Fock G4 method. Bond dissociation energies of monocyclic aromatic hydrocarbons are calculated and used to identify substituent interactions in these radicals. Benson's Group Additivity (GA) scheme is extended to aromatic radicals by defining 6 GAV and 29 NNI parameters through least squares regression to a database of thermodynamic properties of 369 radicals. Comparison between G4/BAC and GA calculated thermodynamic values shows that the standard enthalpies of formation generally agree within 4 kJ mol−1, whereas the entropies and the heat capacities deviate less than 4 J mol−1 K−1. © 2016 American Institute of Chemical Engineers AIChE J, 63: 2089–2106, 2017

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Section: Introductionmentioning

confidence: 99%

Group additive modeling of substituent effects in monocyclic aromatic hydrocarbon radicals

et al. 2016

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“…The validity of generated molecules is examined by converting generated 3D coordinates into canonical SMILES strings using the xyz2mol script from the Jensen group, 39,40 which relies on Rdkit. 34 The conversion could also be done using only Rdkit or other open source tools like Open Babel but they are less reliable when determining bond orders during conversion.…”

Section: Resultsmentioning

confidence: 99%

3D-Scaffold: Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds

Joshi

Gebauer

Bontha

et al. 2021

Preprint

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The prerequisite of therapeutic drug design is to identify novel molecules with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to obtain molecules with desired target properties is the preservation of critical scaffolds in the generation process. To this end, we propose a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of the desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We show that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific datasets, we generate covalent and non-covalent antiviral inhibitors. To measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease and non-structural protein endoribonuclease (NSP15) targets. Most importantly, our model performs well with relatively small volumes of training data and generalizes to new scaffolds, making it applicable to other domains.

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“…If just one atom in the product has more neighbours than allowed, it is discarded. However, if the product AC matrix is valid, we use the program xyz2mol [19,20] to convert the product AC matrix P i into an RDKit mapped molecular graph. To avoid duplicate products, we only save the products graphs with unique SMILES.…”

Section: Systematic Search: Combinatorial Graph Enumerationmentioning

confidence: 99%

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

Rasmussen¹,

Madsen²,

Jensen

2021

Preprint

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<div> <div> <div> <p>We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (<i>J. Chem. Theory. Comput</i>. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in a few hours using modest computational resources. </p> </div> </div> </div>

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Universal Structure Conversion Method for Organic Molecules: From Atomic Connectivity to Three‐Dimensional Geometry

Cited by 56 publications

References 39 publications

Group additive modeling of substituent effects in monocyclic aromatic hydrocarbon radicals

Group additive modeling of substituent effects in monocyclic aromatic hydrocarbon radicals

3D-Scaffold: Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

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