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Showing papers in "ChemRxiv in 2019"


Journal ArticleDOI
01 Jan 2019-ChemRxiv
TL;DR: Benjamin Sanchez Lengeling, Löıc M. Roch, José Daŕıo Perea, Stefan Langner, Christoph J. Brabec, and Alán Aspuru Guzik Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, 02138, USA Institute of Materials for Electronics and Energy Technology (i-MEET), Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstrasse 7, 91058 Erlangens, Germany Bavarian Center for Applied Energy Research (
Abstract: Benjamin Sanchez Lengeling, Löıc M. Roch, José Daŕıo Perea, Stefan Langner, Christoph J. Brabec, and Alán Aspuru-Guzik Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, 02138, USA Institute of Materials for Electronics and Energy Technology (i-MEET), Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstrasse 7, 91058 Erlangen, Germany Bavarian Center for Applied Energy Research (ZAE Bayern), Immerwahrstrasse 2, 91058 Erlangen, Germany Senior Fellow, Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3H7, Canada

59 citations


Journal ArticleDOI
01 Jan 2019-ChemRxiv
TL;DR: A combination of density functional theory (DFT) and machine learning techniques provide a practical method for exploring this parameter space of vdW heterostructures much more efficiently than by DFT or experiments.
Abstract: There are now, in principle, a limitless number of hybrid van der Waals (vdW) heterostructures that can be built from the rapidly growing number of 2D layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work. However, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. A combination of density functional theory (DFT) and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiments. As a proof of concept, this methodology is applied to predict the interlayer distance and band gap of bilayer heterostructures. The methods quickly and accurately predict these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of vdW heterostructures to identify new hybrid materials with useful and interesting properties.

52 citations


Posted ContentDOI
01 Jul 2019-ChemRxiv
TL;DR: The GFN0-xTB method as mentioned in this paper is a semi-empirical quantum chemical method, designed for the fast calculation of molecular Geometries, vibrational frequency and non-covalent interaction energies of systems with up to a few thousand atoms.
Abstract: We propose a semiempirical quantum chemical method, designed for the fast calculation of molecular Geometries, vibrational Frequencies and Non-covalent interaction energies (GFN) of systems with up to a few thousand atoms. Like its predecessors GFN-xTB and GFN2-xTB, the new method termed GFN0-xTB is parameterized for all elements up to radon (Z = 86) and mostly shares well-known density functional tight-binding approximations as well as basis set and integral approximations. The main new feature is the avoidance of the self-consistent charge iterations leading to speed-ups of a factor of 2-20 depending on the size and electronic complexity of the system. This is achieved by including only quantum mechanical contributions up to first-order which are incorporated similar to the previous versions without any pair-specific parameterization. The essential electrostatic electronic interaction is treated by a classical electronegativity equilibration charge model yielding atomic partial charges that enter the electronic Hamiltonian indirectly. Furthermore, the atomic charge-dependent D4 dispersion correction is included to account for long range London correlation effects. Formulas for analytical total energy gradients with respect to nuclear displacements are derived and implemented in the xtb code allowing numerically very precise structure optimizations. The neglect of self-consistent energy terms not only leads to a large gain in computational speed but also can increase robustness in electronically difficult situations because ill-convergence or artificial charge-transfer (CT) is avoided. The comparison of GFN0-xTB and GFN/GFN2-xTB allows dissection of quantum electronic polarization and CT effects thereby improving our understanding of chemical bonding. Compared to the most sophisticated multipole-based GFN2-xTB model (which approaches DFT accuracy for the target properties closely), GFN0-xTB performs slightly worse for non-covalent interactions and molecular structures, while very good results are observed for conformational energies. Vibrational frequencies are obtained less accurately than with GFN/GFN2-xTB but they may still be useful for various purposes like estimating relative thermostatistical reaction energies. Most exceptional is the fact that even relatively complicated transition metal complex structures can be accurately optimized with a non-self-consistent quantum approach. The new method bridges the gap between force-fields and traditional semiempirical methods with its excellent computational cost to accuracy ratio and is intended to explore the chemical space of large molecular systems and solids.

51 citations


Journal ArticleDOI
01 Feb 2019-ChemRxiv
TL;DR: Dual-catalytic transition metal systems that merge a reversible activation cycle with a functionalization cycle, which together enable the functionalization of substrates at their inherently unreactive sites are reported.
Abstract: Catalytic reactions occur readily at the sites of starting materials that are both innately reactive and sterically accessible, or that are predisposed by a functional group amenable to direct a catalyst. However, selective reactions at unbiased sites of substrates remain challenging and typically require additional preactivation steps or the use of highly reactive reagents. Here we report dual-catalytic transition metal systems that merge a reversible activation cycle with a functionalization cycle, which together enable the functionalization of substrates at their inherently unreactive sites. By engaging the Ru- or Fe-catalysed equilibrium between an alcohol and an aldehyde, methods for Pd-catalysed β-arylation of aliphatic alcohols and Rh-catalysed γ-hydroarylation of allylic alcohols were developed. The mild conditions, functional group tolerance and broad scope (81 examples) demonstrate the synthetic applicability of the dual-catalytic systems. This work highlights the potential of the multicatalytic approach to address challenging transformations to circumvent multistep procedures and the use of highly reactive reagents in organic synthesis. The ability to functionalize normally unreactive sites in molecules opens up tremendous flexibility in synthesis design and structural modification, in addition to reducing the need for multiple steps or highly reactive reagents. Now, a dual-catalytic strategy, demonstrated with the methods for the β-arylation of aliphatic alcohols and for the enantioselective γ-hydroarylation of allylic alcohols, is reported for such reactions.

49 citations


Journal ArticleDOI
12 Sep 2019-ChemRxiv
TL;DR: In this article, the efficient removal, capture, and recycling of ammonia (NH3) constitutes a demanding process; thus, the development of competent adsorbent materials is highly desirable.
Abstract: The efficient removal, capture, and recycling of ammonia (NH3) constitutes a demanding process; thus, the development of competent adsorbent materials is highly desirable. The implementation of met...

44 citations


Journal ArticleDOI
01 Nov 2019-ChemRxiv
TL;DR: In this paper, the authors used the first-principles parameterized forcefield MOF-FF to investigate the thermal and pressure induced transformations for nanocrystallites of the pillared-layer DMOF-1.
Abstract: One of the intriguing features of certain metal-organic frameworks (MOFs) is the large volume change upon external stimuli like pressure or guest molecule adsorption, referred to as “breathing”. This displacive phase transformation from an open to a closed pore has been investigated intensively by theoretical simulations within periodic boundary conditions (PBC). However, the actual free energy barriers for the transformation under real conditions and the impact of surface effects on it can only be studied beyond PBC for nanocrystallites. In this work, we used the first-principles parameterized forcefield MOF-FF to investigate the thermal- and pressure induced transformations for nanocrystallites of the pillared-layer DMOF-1 (Zn 2 (bdc) 2 (dabco); bdc: 1,4-benzenedicarboxylate; dabco: 1,4-diazabicyclo[2.2.2]octane) as a model system. By heating of prepared closed pore nanocrystallites of different size, a spontaneous opening is observed within a few tenth of picoseconds with an interface between the closed and open pore phase moving with a velocity of several 100 m/s through the system. The critical nucleation temperature for the opening transition raises with size. On the other hand, by forcing the closing transition with a distance restraint between paddle-wheel units placed on opposite edges of the crystallite, the free energy barrier can be determined by umbrella sampling. As expected, this barrier is substantially lower than the one determined for a concerted process under PBC. Interestingly, the barrier reduces with the size of the crystallite, indicating a hindering surface effect. The results demonstrate the need consider domain boundaries and surfaces, for example by simulations that go beyond PBC and to large system sizes in order to properly predict and describe first order phase transitions in MOFs.

36 citations


Posted ContentDOI
27 Aug 2019-ChemRxiv
TL;DR: In this paper, three imine-linked covalent organic framework (COF) films are incorporated as active layers into separate thin-film composite (TFC) membranes and tested for their ability to reject an organic pollutant surrogate and salt from water.
Abstract: Three imine-linked covalent organic framework (COF) films are incorporated as active layers into separate thin-film composite (TFC) membranes and tested for their ability to reject an organic pollutant surrogate and salt from water. The synthesized membranes consist of a polyacrylonitrile (PAN) support and a TAPB-PDA-H, TAPB-PDA-Me, or TAPB-PDA-Et COF thin film. The latter two COFs direct six methyl and ethyl substituents per tiled hexagon into the pores, respectively, while maintaining the same topology across the series. These substituents decrease the effective pore size of the COF compared to the parent TAPB-PDA-H COF. The TFC TAPB-PDA-Me membrane rejects Rhodamine-WT (R-WT) dye and NaCl better than the TFC TAPB-PDA-H membrane, and the TFC TAPB-PDA-Et membrane exhibits the best rejection overall. The solution-diffusion model used to analyze this permeation behavior indicates that there is a systematic difference in rejection as subsequent pendant groups are added to the interior of the COF pores. Thes...

35 citations


Journal ArticleDOI
15 Feb 2019-ChemRxiv
TL;DR: In this paper, the authors report a comprehensive study of how laminar flow influences polymer structure and composition, showing that the breadth of the residence time distribution has strong, statistical correlations with reaction conversion, polymer molar mass, and dispersity for polymerizations conducted in continuous flow.
Abstract: Continuous-flow chemistry is emerging as an enabling technology for the synthesis of precise polymers. Recent advances in this rapidly growing field have hastened a need for a fundamental understanding of how fluid dynamics in tubular reactors influence polymerizations. Herein, we report a comprehensive study of how laminar flow influences polymer structure and composition. Tracer experiments coupled with in-line UV–Vis spectroscopy demonstrate how viscosity, tubing diameter, and reaction time affect the residence time distribution (RTD) of fluid in reactor geometries relevant for continuous-flow polymerizations. We found that the breadth of the RTD has strong, statistical correlations with reaction conversion, polymer molar mass, and dispersity for polymerizations conducted in continuous flow. These correlations were demonstrated to be general to a variety of different reaction conditions, monomers, and polymerization mechanisms. Additionally, these findings inspired the design of a droplet flow reactor ...

30 citations


Posted ContentDOI
08 Nov 2019-ChemRxiv
TL;DR: In this paper, the use of colloidal quantum dots (QDs) as stabilizing agent for the FAPI perovskite black phase was proposed, which achieved state-of-the-art performance.
Abstract: The extraordinary low non-radiative recombination and band gap versatility of halide perovskites have led to considerable development in optoelectronic devices. However, this versatility is limited by the stability of the perovskite phase, related to the relative size of the different cations and anions. The most emblematic case is that of formamidinium lead iodine (FAPI) black phase, which has the lowest band gap among all 3D lead halide perovskites, but quickly transforms into the non-perovskite yellow phase at room temperature. Efforts to optimize perovskite solar cells have largely focused on the stabilization of FAPI based perovskite structures, often introducing alternative anions and cations. However, these approaches commonly result in a blue-shift of the band gap, which limits the maximum photo-conversion efficiency. Here, we report the use of PbS colloidal quantum dots (QDs) as stabilizing agent for the FAPI perovskite black phase. The surface chemistry of PbS plays a pivotal role, by developing strong bonds with the black phase but weak ones with the yellow phase. As a result, stable FAPI black phase can be formed at temperatures as low as 85°C in just 10 minutes, setting a record of concomitantly fast and low temperature formation for FAPI, with important consequences for industrialization. FAPI thin films obtained through this procedure preserve the original low band gap of 1.5 eV, reach a record open circuit potential (Voc) of 1.105 V -91% of the maximum theoretical Voc- and preserve high efficiency for more than 700 hours. These findings reveal the potential of strategies exploiting the chemi-structural properties of external additives to relax the tolerance factor and optimize the optoelectronic performance of perovskite materials.

26 citations


Journal ArticleDOI
08 Mar 2019-ChemRxiv
TL;DR: This work demonstrates that reaction networks of nucleotides and amino acids should be considered when exploring the emergence of catalytic networks in the context of molecular evolution.
Abstract: Research on prebiotic chemistry and the origins of nucleic acids and proteins has traditionally been focussed on only one or the other. However, if nucleotides and amino acids co-existed on the early Earth, their mutual interactions and reactivity should be considered explicitly. Here we set out to investigate nucleotide/nucleoside formation by simple dehydration reactions of constituent building blocks (sugar, phosphate, and nucleobase) in the presence of different amino acids. We demonstrate the simultaneous formation of glycosidic bonds between ribose, purines, and pyrimidines under mild conditions without catalysts or activated reagents, as well as nucleobase exchange, in addition to the simultaneous formation of nucleotide and nucleoside isomers from several nucleobases. Clear differences in the distribution of glycosylation products are observed when glycine is present. This work demonstrates that reaction networks of nucleotides and amino acids should be considered when exploring the emergence of catalytic networks in the context of molecular evolution. The direct glycosylation of ribose by nucleobases offers an intuitive route to nucleosides, but is known to be challenging under prebiotically plausible reaction conditions. Here, the addition of amino acids is shown to influence the product distribution, and a dynamic exchange of nucleobases between nucleosides and nucleotides is observed.

25 citations


Posted ContentDOI
25 Mar 2019-ChemRxiv
TL;DR: In this article, a chemical Turing machine was proposed to check the same symbol in an unprecedented Avogadro's number of processors, using the Belousov-Zhabotinsky chemical reaction.
Abstract: Every problem in computing can be cast as decision problems of whether strings are or are not in a language. This makes language recognition fundamental for natural and artificial computation. Computer science teaches that computations and language recognition are carried out by three classes of automata, the most complex of which is the Turing machine. Living systems on Earth compute using the complex machinery of biochemistry; in the artificial, computation today is mostly electronic. Thinking of chemical reactions as molecular recognition machines, and without using biochemistry, we formulate the design, realization, construction and operation of one automaton in each class by means of 1-pot, table top chemical reactors: from the simplest, Finite automata, to the most complex, Turing machines. We test them experimentally by transcribing strings of symbols chemically, sequentially feeding them to the reactor and monitoring the physico/chemical output of the reaction. The strings we use for recognition belong to standard computer science languages specific for each class of automata. We find that language acceptance/rejection criteria by an automaton can be formulated in terms of energy considerations. The free energy spent for every word accepted by our chemical Turing machine can be tuned to a constant value associated with the pair machine-language, where for words in the language the entropy rate offsets the energy dissipation. Our Turing machine uses the Belousov-Zhabotinsky chemical reaction and checks the same symbol in an unprecedented Avogadro’s number of processors. Our findings have implications for chemical and general computing, bio-engineering and technology, for the study of the origin and presence of life in other planetary bodies and for artificial biology.

Posted ContentDOI
02 Apr 2019-ChemRxiv
TL;DR: In this article, an automated and high-throughput approach was developed to solve this problem and predict OER overpotential for arbitrary oxide surfaces, and a machine learning model capable of predicting surface coverages and site activity was demonstrated for a number of previously-unstudied IrO2 and IrO3 polymorphs.
Abstract: Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.

Posted ContentDOI
29 Mar 2019-ChemRxiv
TL;DR: It is demonstrated that LYTACs mediate efficient degradation of Apolipoprotein-E4, epidermal growth factor receptor (EGFR), CD71, and programmed death-ligand 1 (PD-L1).
Abstract: Targeted protein degradation is a powerful strategy to address the canonically undruggable proteome. However, current technologies are limited to targets with cytosolically-accessible and ligandable domains. Here, we designed and synthesized conjugates capable of binding both a cell surface lysosome targeting receptor and the extracellular domain of a target protein. These lysosome targeting chimeras (LYTACs) consist of an antibody fused to agonist glycopeptide ligands for the cation-independent mannose-6-phosphate receptor (CI-M6PR). LYTACs enabled a CRISPRi knockdown screen revealing the biochemical pathway for CI-M6PR-mediated cargo internalization. We demonstrated that LYTACs mediate efficient degradation of Apolipoprotein-E4, epidermal growth factor receptor (EGFR), CD71, and programmed death-ligand 1 (PD-L1). LYTACs represent a modular strategy for directing secreted and membrane proteins for degradation in the context of both basic research and therapy.

Journal ArticleDOI
06 Jun 2019-ChemRxiv
TL;DR: A class of perfunctionalized dodecaborate clusters that exhibit high stability towards high concentration electrochemical cycling are reported, illustrating the potential of bespoke boron clusters as robust material platform for electrochemical energy conversion and storage.
Abstract: We report a class of perfunctionalized dodecaborate clusters that exhibit stability during galvanostatic cycling. These boron clusters afford several degrees of freedom in material design to tailor...

Journal ArticleDOI
05 Jun 2019-ChemRxiv
TL;DR: Many emerging technologies depend on our ability to control and manipulate the excited-state properties of molecular systems as mentioned in this paper, such as fluorescent labeling in biomedical imaging, biomedical imaging and medical imaging.
Abstract: Many emerging technologies depend on our ability to control and manipulate the excited-state properties of molecular systems. These technologies include fluorescent labeling in biomedical imaging, ...

Posted ContentDOI
23 Dec 2019-ChemRxiv
TL;DR: The cyclo[18] carbon has been theoretically and experimentally investigated since long time ago, but only very recently it was prepared and directly observed by means of STM/AFM in condensed phase (Kaiser et al., 2019).
Abstract: Although cyclo[18]carbon has been theoretically and experimentally investigated since long time ago, only very recently it was prepared and directly observed by means of STM/AFM in condensed phase (Kaiser et al., Science, 365, 1299 (2019)). The unique ring structure and dual 18-center π delocalization feature bring a variety of unusual characteristics and properties to the cyclo[18]carbon, which are quite worth to be explored. In this work, we present an extremely comprehensive and detailed investigation on almost all aspects of the cyclo[18]carbon, including (1) Geometric characteristics (2) Bonding nature (3) Electron delocalization and aromaticity (4) Intermolecular interaction (5) Reactivity (6) Electronic excitation and UV/Vis spectrum (7) Molecular vibration and IR/Raman spectrum (8) Molecular dynamics (9) Response to external field (10) Electron ionization, affinity and accompanied process (11) Various molecular properties. We believe that our full characterization of the cyclo[18]carbon will greatly deepen researchers' understanding of this system, and thereby help them to utilize it in practice and design its various valuable derivatives.

Posted ContentDOI
04 Jun 2019-ChemRxiv
TL;DR: Path-Augmented Graph Transformer Networks (PAGTNets) as mentioned in this paper use path features in molecular graphs to create global attention layers, which are explicitly built on longer-range dependencies in graph structured data.
Abstract: Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN). These models rely on local aggregation operations and can therefore miss higher-order graph properties. To remedy this, we propose Path-Augmented Graph Transformer Networks (PAGTN) that are explicitly built on longer-range dependencies in graphstructured data. Specifically, we use path features in molecular graphs to create global attention layers. We compare our PAGTN model against the GCN model and show that our model consistentlyoutperforms GCNs on molecular property prediction datasets including quantum chemistry (QM7, QM8, QM9), physical chemistry (ESOL, Lipophilictiy) and biochemistry (BACE, BBBP)2.

Posted ContentDOI
25 Jul 2019-ChemRxiv
TL;DR: This paper showed that the lack of rotational invariance of popular DFT integration grids reveals large uncertainties in computed free energies for isomerizations, torsional barriers, and regio-and stereoselective reactions.
Abstract: Density functional theory (DFT) has emerged as a powerful tool for analyzing organic and organometallic systems and proved remarkably accurate in computing the small free energy differences that underpin many chemical phenomena (e.g. regio- and stereoselective reactions). We show that the lack of rotational invariance of popular DFT integration grids reveals large uncertainties in computed free energies for isomerizations, torsional barriers, and regio- and stereoselective reactions. The result is that predictions based on DFT-computed free energies for many systems can change qualitatively depending on molecular orientation. For example, for a metal-free propargylation of benzaldehyde, predicted enantioselectivities based on B97-D/def2-TZVP free energies using the popular (75,302) integration grid can vary from 62:38 to 99:1 by simply rotating the transition state structures. Relative free energies for the regiocontrolling transition state structures for an Ir-catalyzed C–H functionalization reaction computed using M06/6-31G(d,p)/LANL2DZ and the same grid can vary by more than 5 kcal/mol, resulting in predicted regioselectivities that range anywhere from 14:86 to >99:1. Errors of these magnitudes occur for different functionals and basis sets, are widespread among modern applications of DFT, and can be reduced by using much denser integration grids than commonly employed.

Journal ArticleDOI
21 Feb 2019-ChemRxiv
TL;DR: In this paper, the authors identify close spectral matches between the surface vibrational spectra of β-caryophyllene-derived secondary organic material (SOM) and those of β -caryophyllene aldehyde and β-carthyophyllonic acid at various interfaces, and discuss possibilities for an intrinsically chemical origin for cloud activation by terpene derived surfactants.
Abstract: By integrating organic synthesis, secondary organic aerosol synthesis and collection, density functional theory (DFT) calculations, and vibrational sum frequency generation (SFG) spectroscopy, we identify close spectral matches between the surface vibrational spectra of β-caryophyllene-derived secondary organic material (SOM) and those of β-caryophyllene aldehyde and β-caryophyllonic acid at various interfaces. Combined with the record high surface tension depression described previously for these same oxidation products, we discuss possibilities for an intrinsically chemical origin for cloud activation by terpene-derived surfactants. Although the present study does not unequivocally identify the synthesized and analyzed oxidation products on the β-caryophyllene-derived SOM surfaces, these two compounds appear to be the most surface active out of the series and have also been foci of previous β-caryophyllene field and laboratory studies. An orientation analysis by phase-resolved SFG spectroscopy reveals a...

Posted ContentDOI
17 Jul 2019-ChemRxiv
TL;DR: In this paper, a structurally diverse family of 39covalent triazine-based framework materials (CTFs) is synthesized by Suzuki-Miyaura polycondensation and tested as hydrogen evolution photocatalysts using a high-throughput workflow.
Abstract: A structurally diverse family of 39 covalent triazine-based framework materials (CTFs) is synthesized by Suzuki-Miyaura polycondensation and tested as hydrogen evolution photocatalysts using a high-throughput workflow. The two best-performing CTFs are based on benzonitrile and dibenzo[b,d]thiophene sulfone linkers, respectively, with catalytic activities that are among the highest for this material class. The activities of the different CTFs are rationalized in terms of four variables: the predicted electron affinity, the predicted ionization potential, the optical gap, and the dispersibility of the CTFs particles in solution, as measured by optical transmittance. The electron affinity and dispersibility in solution are the best predictors of photocatalytic hydrogen evolution activity.

Journal ArticleDOI
02 Dec 2019-ChemRxiv
TL;DR: This work presents an analysis of the whole cell and plasma membrane lipid isolates of a neuroblastoma cell line, SH-SY5Y, a commonly used model system for research on this and other neurodegenerative diseases, using two complementary lipidomics methods.
Abstract: Global lipid analysis still lags behind proteomics with respect to the availability of databases, experimental protocols, and specialized software. Determining the lipidome of cellular model systems in common use is of particular importance, especially when research questions involve lipids directly. In Parkinson's disease research, there is a growing awareness for the role of the biological membrane, where individual lipids may contribute to provoking α-synuclein oligomerisation and fibrillation. We present an analysis of the whole cell and plasma membrane lipid isolates of a neuroblastoma cell line, SH-SY5Y, a commonly used model system for research on this and other neurodegenerative diseases. We have used two complementary lipidomics methods. The relative quantities of PC, PE, SMs, CL, PI, PG, and PS were determined by 31P NMR. Fatty acid chain composition and their relative abundances within each phospholipid group were evaluated by liquid chromatography-tandem mass spectrometry. For this part of the analysis, we have developed and made available a set of Matlab scripts, LipMat. Our approach allowed us to observe several deviations of lipid abundances when compared to published reports regarding phospholipid analysis of cell cultures or brain matter. The most striking was the high abundance of PC (54.7 ± 1.9%) and low abundance of PE (17.8 ± 4.8%) and SMs (2.7 ± 1.2%). In addition, the observed abundance of PS was smaller than expected (4.7 ± 2.7%), similar to the observed abundance of PG (4.5 ± 1.8%). The observed fatty acid chain distribution was similar to the whole brain content with some notable differences: a higher abundance of 16:1 PC FA (17.4 ± 3.4% in PC whole cell content), lower abundance of 22:6 PE FA (15.9 ± 2.2% in plasma membrane fraction), and a complete lack of 22:6 PS FA.

Journal ArticleDOI
11 Nov 2019-ChemRxiv
TL;DR: In this article, a saddle-like π-conjugated cyclooctatetrathiophene core with the N-annulated perylene diimide (PDI) chromophor...
Abstract: This contribution explores the direct (hetero)arylation (DHA) cross-coupling of a saddle-like, π-conjugated cyclooctatetrathiophene (Th4) core with the N-annulated perylene diimide (PDI) chromophor...

Journal ArticleDOI
21 Nov 2019-ChemRxiv
TL;DR: Results demonstrate that proximity-induced photolabelling is applicable to interfaces that mediate protein-protein interactions, and pave the way towards future use of ligand-directed proximity labelling for dynamic analysis of the interactome of BCL-2 family proteins.
Abstract: Ligand-directed protein labelling allows the introduction of diverse chemical functionalities onto proteins without the need for genetically encoded tags. Here we report a method for the rapid labelling of a protein using a ruthenium-bipyridyl (Ru(II)(bpy)3)-modified peptide designed to mimic an interacting BH3 ligand within a BCL-2 family protein-protein interactions. Using sub-stoichiometric quantities of (Ru(II)(bpy)3)-modified NOXA-B and irradiation with visible light for 1 min, the anti-apoptotic protein MCL-1 can be photolabelled with a variety of functional tags. In contrast with previous reports on Ru(II)(bpy)3-mediated photolabelling, tandem mass spectrometry experiments reveal that the labelling site is a cysteine residue of MCL-1. MCL-1 can be labelled selectively in mixtures with other proteins, including the structurally related BCL-2 member, BCL-xL. These results demonstrate that proximity-induced photolabelling is applicable to interfaces that mediate protein-protein interactions, and pave the way towards future use of ligand-directed proximity labelling for dynamic analysis of the interactome of BCL-2 family proteins. Ligand-directed protein labeling allows selective modification of native proteins but typically requires stoichiometric quantities of the labeling agent. Here a substoichiometric quantity of a peptide probe bound to a photocatalyst allows selective labeling of a target protein Cys residue in the presence of structurally similar proteins.

Posted ContentDOI
26 Feb 2019-ChemRxiv
TL;DR: A new molecular simulation package that is designed for ab initio molecular dynamics simulations of molecular and condensed-phase chemical reactions and other processes, with particular focus on mean-field and quantum embedding methods for electronic structure is described.
Abstract: entos is designed for ab initio MD simulations of molecular and condensed-phase chemical reactions and other processes, with particular focus on mean-field and quantum embedding methods for electronic structure. The entos software package is developed in the C++14 programming language with a structure that enables flexibility (by providing a long-term sustainable platform for development of methods in this area), efficiency (via task-based multi-threaded parallelism), and rigorous software engineering standards.

Journal ArticleDOI
05 Aug 2019-ChemRxiv
TL;DR: A set of substituted 9,10-dicyanoanthracenes (DCA) has been synthesized, their photophysical and electrochemical properties in liquid solution have been characterized and supplemented by high level ab initio quantum chemical calculations as mentioned in this paper.
Abstract: A set of substituted 9,10-dicyanoanthracenes (DCA) has been synthesized, their photophysical and electrochemical properties in liquid solution have been characterized and supplemented by high level ab initio quantum chemical calculations. Three different methoxy-group-containing substituents have been linked to the DCA core in a symmetric and asymmetric fashion to produce six different species with strong quadrupole and dipole moments, respectively. The major difference between the symmetrically and asymmetrically substituted species are the enhanced two-photon absorption intensities of the former. In most of the cases studied, the molecules show reasonably large optical transition probabilities. The fluorescence brightness of these substances makes them interesting objects for two-photon absorption applications. Additionally, all molecules can be both easily reduced and oxidized electrochemically and are therefore suitable for optoelectronic applications.

Posted ContentDOI
11 Apr 2019-ChemRxiv
TL;DR: The Penalized Variational Autoencoder is introduced which directly operates on SMILES, a linear string representation of molecules, with a weight penalty term in the decoder to address the imbalance in the character distribution ofSMILES strings.
Abstract: Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously. To do so, we introduce the Penalized Variational Autoencoder which directly operates on SMILES, a linear string representation of molecules, with a weight penalty term in the decoder to address the imbalance in the character distribution of SMILES strings. We find that this greatly improves upon previous variational autoencoder approaches in the quality of the latent space and the generalization ability of the latent space to new chemistry. Next, we organize the latent space according to chemical and biological properties by jointly training the Penalized Variational Autoencoder with linear units. Extensive experiments on a range of tasks, including reconstruction, validity, and transferability demonstrates that the proposed methods here substantially outperform previous SMILES and graph-based methods, as well as introduces a new way to generate molecules from a set of desired properties, without prior knowledge of a chemical structure.

Journal ArticleDOI
05 Aug 2019-ChemRxiv
TL;DR: In this paper, the fundamental design of piezoelectric materials from the ground up has been studied and a general, general, and general approach has been proposed for the design of these materials.
Abstract: Despite considerable research interest in developing piezoelectric materials, little work has focused on the fundamental design of these materials from the ground up. Herein, we present a general, ...

Posted ContentDOI
30 Jul 2019-ChemRxiv
TL;DR: In this article, a graph convolutional neural network (GCNN) was proposed for molecular property prediction in industrial workflows and compared with existing graph neural network architectures on both public and proprietary datasets.
Abstract: Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial datasets spanning a wide variety of chemical endpoints. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.

Posted ContentDOI
06 May 2019-ChemRxiv
TL;DR: In this article, the authors proposed a density functionals from coupled-cluster energies, based only on Kohn-Sham density functional theory (DFT) via machine learning.
Abstract: Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal/mol with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. We create density functionals from coupled-cluster energies, based only on DFT densities, via machine learning. These functionals attain quantum chemical accuracy (errors below 1 kcal/mol). Moreover, density-based ∆-learning (learning only the correction to a standard DFT calculation, ∆-DFT) significantly reduces the amount of training data required. We demonstrate these concepts for a single water molecule, and then illustrate how to include molecular symmetries with ethanol. Finally, we highlight the robustness of ∆-DFT by correcting DFT simulations of resorcinol on the fly to obtain molecular dynamics (MD) trajectories with coupled-cluster accuracy. Thus ∆-DFT opens the door to running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT is quantitatively incorrect.

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13 Jun 2019-ChemRxiv
TL;DR: In this paper, a graph-to-graph translation method for molecular optimization is proposed, which realizes coherent multi-resolution representations by interweaving trees over substructures with the atom-level encoding of the original molecular graph.
Abstract: The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving trees over substructures with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its connectivity to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines by a large margin.