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1.
Mol Pharm ; 17(7): 2612-2627, 2020 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-32459098

RESUMO

Nanosystems are gaining momentum in pharmaceutical sciences because of the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer cotherapy drug-vitamin release nanosystems (DVRNs) including anticancer compounds and vitamins or vitamin derivatives have revealed encouraging results. However, the number of possible combinations of design and synthesis conditions is remarkably high. In addition, a large number of anticancer and vitamin derivatives have been already assayed, but a notably less number of cases of DVRNs were assayed as a whole (with the anticancer compound and the vitamin linked to them). Our approach combines with the perturbation theory and machine learning (PTML) model to predict the probability of obtaining an interesting DVRN by changing the anticancer compound and/or the vitamin present in a DVRN that is already tested for other anticancer compounds or vitamins that have not been tested yet as part of a DVRN. In a previous work, we built a linear PTML model useful for the design of these nanosystems. In doing so, we used information fusion (IF) techniques to carry out data enrichment of DVRN data compiled from the literature with the data for preclinical assays of vitamins from the ChEMBL database. The design features of DVRNs and the assay conditions of nanoparticles (NPs) and vitamins were included as multiplicative PT operators (PTOs) to the system, which indicates the importance of these variables. However, the previous work omitted experiments with nonlinear ML techniques and different types of PTOs such as metric-based PTOs. More importantly, the previous work does not consider the structure of the anticancer drug to be included in the new DVRNs. In this work, we are going to accomplish three main objectives (tasks). In the first task, we found a new model, alternative to the one published before, for the rational design of DVRNs using metric-based PTOs. The most accurate PTML model was the artificial neural network model, which showed values of specificity, sensitivity, and accuracy in the range of 90-95% in training and external validation series for more than 130,000 cases (DVRNs vs ChEMBL assays). Furthermore, in the second task, we used IF techniques to carry out data enrichment of our previous data set. In doing so, we constructed a new working data set of >970,000 cases with the data of preclinical assays of DVRNs, vitamins, and anticancer compounds from the ChEMBL database. All these assays have multiple continuous variables or descriptors dk and categorical variables cj (conditions of the assays) for drugs (dack, cacj), vitamins (dvk, cvj), and NPs (dnk, cnj). These data include >20,000 potential anticancer compounds with >270 protein targets (cac1), >580 assay cell organisms (cac2), and so forth. Furthermore, we include >36,000 assay vitamin derivatives in >6200 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit), and so forth. The enriched data set also contains >20 types of DVRNs (c5n) with 9 NP core materials (c4n), 8 synthesis methods (c7n), and so forth. We expressed all this information with PTOs and developed a qualitatively new PTML model that incorporates information of the anticancer drugs. This new model presents 96-97% of accuracy for training and external validation subsets. In the last task, we carried out a comparative study of ML and/or PTML models published and described how the models we are presenting cover the gap of knowledge in terms of drug delivery. In conclusion, we present here for the first time a multipurpose PTML model that is able to select NPs, anticancer compounds, and vitamins and their conditions of assay for DVRN design.

2.
J Chem Inf Model ; 59(4): 1357-1365, 2019 04 22.
Artigo em Inglês | MEDLINE | ID: mdl-30897905

RESUMO

Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors.

3.
J Phys Chem Lett ; 9(18): 5339-5343, 2018 Sep 20.
Artigo em Inglês | MEDLINE | ID: mdl-30145896

RESUMO

Copper surfaces exhibit high catalytic selectivity but have poor hydrogen dissociation kinetics; therefore, we consider icosahedral Cu13 nanoclusters to understand how nanoscale structure might improve catalytic prospects. We find that the spin state is a surprisingly important design consideration. Cu13 clusters have large magnetic moments due to finite size and symmetry effects and exhibit magnetization-dependent catalytic behavior. The most favorable transition state for hydrogen dissociation has a lower activation energy than that on single-crystal copper surfaces but requires a magnetization switch from 5 to 3 µB. Without this switch, the activation energy is higher than that on single-crystal surfaces. Weak spin-orbit coupling hinders this switch, decreasing the kinetic rate of hydrogen dissociation by a factor of 16. We consider strategies to facilitate magnetization switches through optical excitations, substitution, charge states, and co-catalysts; these considerations demonstrate how control of magnetic properties could improve catalytic performance.

4.
Chem Rev ; 118(5): 2816-2862, 2018 03 14.
Artigo em Inglês | MEDLINE | ID: mdl-29116787

RESUMO

The activation of O2 on metal surfaces is a critical process for heterogeneous catalysis and materials oxidation. Fundamental studies of well-defined metal surfaces using a variety of techniques have given crucial insight into the mechanisms, energetics, and dynamics of O2 adsorption and dissociation. Here, trends in the activation of O2 on transition metal surfaces are discussed, and various O2 adsorption states are described in terms of both electronic structure and geometry. The mechanism and dynamics of O2 dissociation are also reviewed, including the importance of the spin transition. The reactivity of O2 and O toward reactant molecules is also briefly discussed in the context of catalysis. The reactivity of a surface toward O2 generally correlates with the adsorption strength of O, the tendency to oxidize, and the heat of formation of the oxide. Periodic trends can be rationalized in terms of attractive and repulsive interactions with the d-band, such that inert metals tend to feature a full d band that is low energy and has a large spatial overlap with adsorbate states. More open surfaces or undercoordinated defect sites can be much more reactive than close-packed surfaces. Reactions between O and other species tend to be more prevalent than reactions between O2 and other species, particularly on more reactive surfaces.

5.
J Chem Phys ; 146(21): 214703, 2017 Jun 07.
Artigo em Inglês | MEDLINE | ID: mdl-28576088

RESUMO

The surface structure and composition of a multi-component catalyst are critical factors in determining its catalytic performance. The surface composition can depend on the local pressure of the reacting species, leading to the possibility that the flow through a nanoporous catalyst can affect its structure and reactivity. Here, we explore this possibility for oxidation reactions on nanoporous gold, an AgAu bimetallic catalyst. We use microscopy and digital reconstruction to obtain the morphology of a two-dimensional slice of a nanoporous gold sample. Using lattice Boltzmann fluid dynamics simulations along with thermodynamic models based on first-principles total-energy calculations, we show that some sections of this sample have low local O2 partial pressures when exposed to reaction conditions, which leads to a pure Au surface in these regions, instead of the active bimetallic AgAu phase. We also explore the effect of temperature on the surface structure and find that moderate temperatures (≈300-450 K) should result in the highest intrinsic catalytic performance, in apparent agreement with experimental results.

6.
Phys Chem Chem Phys ; 18(38): 26844-26853, 2016 Sep 29.
Artigo em Inglês | MEDLINE | ID: mdl-27722678

RESUMO

One of the most critical factors in oxidation catalysis is controlling the state of oxygen on the surface. Au and Ag are both effective selective oxidation catalysts for various reactions, and their interactions with oxygen are critical for determining their catalytic performance. Here, we show that the state of oxygen on a catalytic surface can be controlled by alloying Au and Ag. Using temperature programmed desorption, density functional theory (DFT), and Monte Carlo simulations, we examine how alloying Au into an Ag(110) surface affects O2 dissociation, O coverage, and O stability. DFT calculations indicate that Au resides in the second layer, in agreement with previous experimental findings. The minimum ensemble size for O2 dissociation is 2 Ag atoms in adjacent rows of the second layer. Surprisingly, adsorbed O2 and the dissociation transition state interact directly with metal atoms in the adjacent trough, such that Au in this position inhibits O2 dissociation by direct repulsion with oxygen electronic states. Using Monte Carlo simulations based on DFT energetics, we create models of the surface that agree closely with our experimental results. Both show that the O2 uptake decreases nearly linearly as the Au concentration increases, and no O2 uptake occurs for Au concentrations above 50%. For Au concentrations greater than 18%, increasing the Au concentration also decreases the stability of the adsorbed O. Based on these results, the O coverage and O stability can be tuned, in some cases independently. We also study how the reactivity of the surface is affected by these factors using CO2 oxidation as a simple test reaction.

7.
J Chem Phys ; 145(7): 074702, 2016 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-27544118

RESUMO

Hydrocarbon chains are important intermediates in various aqueous-phase surface processes, such as CO2 electroreduction, aqueous Fischer-Tropsch synthesis, and aqueous phase reforming of biomass-derived molecules. Further, the interaction between water and adsorbed hydrocarbons represents a difficult case for modern computational methods. Here, we explore various methods for calculating the energetics of this interaction within the framework of density functional theory and explore trade-offs between the use of low water coverages, molecular dynamics approaches, and minima hopping for identification of low energy structures. An effective methodology for simulating low temperature processes is provided by using a unit cell in which the vacuum space is filled with water, employing the minima hopping algorithm to search for low-lying minima, and including dispersion (van der Waals) interactions. Using this methodology, we show that a high coverage of adsorbed alkyls is destabilized by the presence of water, while a low coverage of alkyls is stabilized. Solvation has a small effect on the energetics of hydrocarbon chain growth, generally decreasing its favorability at low temperatures. We studied higher temperatures by running molecular dynamics simulations starting at the minima found by the minima hopping algorithm and found that increased temperatures facilitate chain growth. The self-consistent continuum solvation method effectively describes the alkyl-water interaction and is in general agreement with the explicit solvation results in most cases, but care should be taken at high alkyl coverage.

8.
Philos Trans A Math Phys Eng Sci ; 374(2061)2016 Feb 28.
Artigo em Inglês | MEDLINE | ID: mdl-26755756

RESUMO

Decreasing energy consumption in the production of platform chemicals is necessary to improve the sustainability of the chemical industry, which is the largest consumer of delivered energy. The majority of industrial chemical transformations rely on catalysts, and therefore designing new materials that catalyse the production of important chemicals via more selective and energy-efficient processes is a promising pathway to reducing energy use by the chemical industry. Efficiently designing new catalysts benefits from an integrated approach involving fundamental experimental studies and theoretical modelling in addition to evaluation of materials under working catalytic conditions. In this review, we outline this approach in the context of a particular catalyst-nanoporous gold (npAu)-which is an unsupported, dilute AgAu alloy catalyst that is highly active for the selective oxidative transformation of alcohols. Fundamental surface science studies on Au single crystals and AgAu thin-film alloys in combination with theoretical modelling were used to identify the principles which define the reactivity of npAu and subsequently enabled prediction of new reactive pathways on this material. Specifically, weak van der Waals interactions are key to the selectivity of Au materials, including npAu. We also briefly describe other systems in which this integrated approach was applied.

9.
Nat Chem ; 7(5): 378-80, 2015 May.
Artigo em Inglês | MEDLINE | ID: mdl-25901813
10.
J Am Chem Soc ; 136(26): 9272-5, 2014 Jul 02.
Artigo em Inglês | MEDLINE | ID: mdl-24931651

RESUMO

A key issue in catalyst design is understanding how adsorption energies of surface intermediates vary across both different surfaces and various types of adsorbing atoms. In this work, we examine trends in adsorption energies of a wide variety of adsorbates that attach to transition metal surfaces through different atoms (H, C, N, O, F, S, etc.). All adsorption energies, as calculated by density functional theory, have nearly identical dependence on the metal bands (the d-band center and the number of p electrons) and the adsorbates' highest occupied molecular orbital (HOMO) energies. However, the dependence on the adsorbate-surface coupling and the d-band filling varies with the energy of the HOMO. Adsorbates with low HOMOs experience a higher level of Pauli repulsion than those with higher HOMOs. This leads to a classification of adsorbates into two groups, where adsorption energies in each group correlate. Even across the groups, adsorbates with similar HOMO energies are likely to have correlated adsorption energies.

11.
J Chem Phys ; 136(20): 204710, 2012 May 28.
Artigo em Inglês | MEDLINE | ID: mdl-22667584

RESUMO

To better understand the nature of alkyl intermediates often invoked in reactions involving hydrocarbon reactants and products, the adsorption of linear and branched C(1)-C(4) alkyls on Cu(111) at 1/4 ML and 1/9 ML coverages was studied using density functional theory. The adsorption energy and site preference are found to be coverage-dependent, and both direct alkyl-alkyl interactions and changes in the Cu electronic structure play a role in these trends. It was found that methyl strongly prefers the hollow sites, the branched alkyls strongly prefer the top site, and the linear C(2)-C(4) alkyls have weak site preferences that change with coverage. To explain these differences, rationalize alkyl adsorption trends, and predict the binding energy of other alkyls, a simple model was developed in which the binding energy is fit as a linear function of the number of C-Cu and C-H-Cu interactions as well as the C-H bond energy in the corresponding alkane. Site preference can be understood as a compromise between C-Cu interactions and C-H-Cu interactions. Density of states analysis was used to gain a molecular-orbital understanding of the bonding of alkyls to Cu(111).

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