Combinatorial Computational Chemistry Research Articles

Combinatorial Computational Chemistry Approach for Materials Design: Applications in DeNOx Catalysis, Fischer‐Tropsch Synthesis, Lanthanoid Complex, and Lithium Ion Secondary Battery.

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Combinatorial Computational Chemistry Approach for Materials Design:Applications in deNOx Catalysis, Fischer-Tropsch Synthesis, Lanthanoid Complex, and Lithium Ion Secondary Battery

Computational chemistry can provide fundamental knowledge regarding various aspects of materials. While its impact in scientific research is greatly increasing, its contributions to industrially important issues are far from satisfactory. In order to realize industrial innovation by computational chemistry, a new concept "combinatorial computational chemistry" has been proposed by introducing the concept of combinatorial chemistry to computational chemistry. This combinatorial computational chemistry approach enables theoretical high-throughput screening for materials design. In this manuscript, we review the successful applications of combinatorial computational chemistry to deNO(x) catalysts, Fischer-Tropsch catalysts, lanthanoid complex catalysts, and cathodes of the lithium ion secondary battery.

Combinatorial computational chemistry approach of tight-binding quantum chemical molecular dynamics method to the design of the automotive catalysts

Recently, we have developed a new tight-binding quantum chemical molecular dynamics program “Colors” for combinatorial computational chemistry approach. This methodology is based on our original tight-binding approximation and realized over 5000 times acceleration compared to the conventional first-principles molecular dynamics method. In the present study, we applied our new program to the simulations on various realistic large-scale models of the automotive three-way catalysts, ultrafine Pt particle/CeO 2(111) support. Significant electron transfer from the Pt particle to the CeO 2(111) surface was observed and it was found to strongly depend on the size of the Pt particle. Furthermore, our simulation results suggest that the reduction of the Ce atom due to the electron transfer from the Pt particle to the CeO 2 surface is a main reason for the strong interaction of the Pt particle and CeO 2(111) support.

05/00874 Design of new catalysts for ecological high-quality transportation fuels by combinatorial computational chemistry and tight-binding quantum chemical molecular dynamics approaches

05/00874 Design of new catalysts for ecological high-quality transportation fuels by combinatorial computational chemistry and tight-binding quantum chemical molecular dynamics approaches

The design of drug candidate molecules as selective inhibitors of therapeutically relevant protein kinases.

The human genome encompasses some 2,000 proteins that utilize adenosine 5'-triphosphate (ATP) in one way or another and some 500 of these are protein-tyrosine and protein-serine/threonine kinases (PTKs & PSTKs). Substrate phosphorylation by these enzymes is nature's predominant molecular way of organizing cellular signal transduction and regulating biochemical processes in general. It is not surprising, therefore, that abnormal phosphorylation of cellular proteins is a hallmark of disease and that there has been a growing interest in the use of kinase inhibitors as drugs. In fact the search for such agents has recently culminated in the approval of the first kinase inhibitor drugs for medical use. Although it has been demonstrated exhaustively that potent and structurally diverse ATP-antagonistic small molecule kinase inhibitors can be found through mass screening and structure-guided design, the question of biochemical, cellular, and in vivo selectivity of such inhibitors remains much less clear. Here the medicinal chemistry of kinase inhibitors is reviewed critically with particular emphasis on target selectivity and specificity. Approaches based on chemical genomics, combinatorial target-guided ligand assembly, computational chemistry, and structural biology techniques, which aim at classifying both inhibitors and kinase targets, are given special emphasis. The various strategies in which differences in biochemical mechanism of kinase function can be exploited in order to attain selective inhibition are also discussed. Furthermore, recent developments in the design of inhibitors to selected individual validated therapeutic kinase targets, including cell cycle kinases and receptor PTKs, etc. are summarised.

Design of new catalysts for ecological high-quality transportation fuels by combinatorial computational chemistry and tight-binding quantum chemical molecular dynamics approaches

Recently, we introduced a concept of combinatorial chemistry to computational chemistry and proposed a new method called “combinatorial computational chemistry”, which enables us to perform a theoretical high-throughput screening of catalysts. In the present paper, we reviewed our recent application of our combinatorial computational chemistry approach to the design of new catalysts for high-quality transportation fuels. By using our combinatorial computational chemistry techniques, we succeeded to predict new catalysts for methanol synthesis and Fischer–Tropsch synthesis. Moreover, we have succeeded in the development of chemical reaction dynamics simulator based on our original tight-binding quantum chemical molecular dynamics method. This program realizes more than 5000 times acceleration compared to the regular first-principles molecular dynamics method. Electronic- and atomic-level information on the catalytic reaction dynamics at reaction temperatures significantly contributes the catalyst design and development. Hence, we also summarized our recent applications of the above quantum chemical molecular dynamics method to the clarification of the methanol synthesis dynamics in this review.

Combinatorial Computational Chemistry Approach in the Design of New Catalysts and Functional Materials

Three decades ago, J. J. Hanak proposed a new approach for materials exploration that he called the ‘‘multisample concept.’’ The goal was to make materials discovery processes faster with higher efficiency as compared to the standard methods (1). This idea of producing many different compositions at once appeared to be an elegant art that introduced diversity and parallel processing in experiments. More recently, researchers have begun to use a new idea commonly known as the combinatorial approach. At first, this was applied mostly to chemical synthesis and screening of large numbers of organic compounds for drug discovery processes (2). By using the combinatorial approach, researchers have been able to predict and find an optimal composition with the desired characteristics much faster than before. There are many good examples of applications of the combinatorial method in chemistry, including the development of catalytic antibodies (3) and, more recently, the identification of nonpeptide agonists (activators) for different somatostain receptor subtypes (4). Inorganic materials have many more potential applications than organic materials because they exhibit a wide range of physical/chemical properties, many of which are strongly dependent on compositions. The combinatorial approach has great potential in investigating important inorganic materials, and its utility

Combinatorial Computational Chemistry

A new methodology “Combinatorial Computational Chemistry” was proposed to realize theoretical high-throughput screening of materials. In this review, we introduced our recent successful applications of combinatorial computational chemistry to material design. Especially, we succeeded in the development of huge number of new simulation programs for combinatorial computational chemistry. For example, our SCF-tight-binding quantum chemical molecular dynamics program is very effective to realize high-throughput screening, since it realizes more than 5,000 times acceleration. Furthermore, we succeeded in the development of multi-physics quantum chemical molecular dynamics program based on our SCF-tight-binding theory, which can simulate multi-physics phenomena including chemical reaction, shear, impact, stress, flow, electric field and so on. This new program realizes various process design in addition to material design on the basis of the combinatorial computational chemistry. We emphasized here that our new simulation programs are very powerful tool to realize high-throughput screening for both material and process design, which cannot be accomplished by the traditional first-principles approach.

Development of tight-binding, chemical-reaction-dynamics simulator for combinatorial computational chemistry

Recently, we have proposed a new concept called “combinatorial computational chemistry” to realize a theoretical, high-throughput screening of catalysts and materials. We have already applied our combinatorial, computational-chemistry approach, mainly based on static first-principles calculations, to various catalysts and materials systems and its applicability to the catalysts and materials design was strongly confirmed. In order to realize more effective and efficient combinatorial, computational-chemistry screening, a high-speed, chemical-reaction-dynamics simulator based on quantum-chemical, molecular-dynamics method is essential. However, to the best of our knowledge, there is no chemical-reaction-dynamics simulator, which has an enough high-speed ability to perform a high-throughput screening. In the present study, we have succeeded in the development of a chemical-reaction-dynamics simulator based on our original, tight-binding, quantum-chemical, molecular-dynamics method, which is more than 5000 times faster than the regular first-principles, molecular-dynamics method. Moreover, its applicability and effectiveness to the atomistic clarification of the methanol-synthesis dynamics at reaction temperature were demonstrated.

Combinatorial computational chemistry approach to the design of metal catalysts for deNO x

Combinatorial chemistry is an efficient technique for the synthesis and screening of a large number of compounds. Recently, we introduced the combinatorial approach to computational chemistry for catalyst design and proposed a new method called “combinatorial computational chemistry”. In the present study, we have applied this combinatorial computational chemistry approach to the design of precious metal catalysts for deNO x . As the first step of the screening of the metal catalysts, we studied Rh, Pd, Ag, Ir, Pt, and Au clusters regarding the adsorption properties towards NO molecule. It was demonstrated that the energetically most stable adsorption state of NO on Ir model cluster, which was irrespective of both the shape and number of atoms including the model clusters.

Combinatorial Computational Chemistry Approach to the High-Throughput Screening of Metal Sulfide Catalysts for CO Hydrogenation Process

We have already proposed that a “Combinatorial Computational Chemistry” approach is very effective for performing the theoretical high-throughput screening of new catalysts, and its validity was strongly confirmed in various catalyst systems. In the present study, we applied our combinatorial computational chemistry approach to the design of new metal sulfide catalysts for the CO hydrogenation process and proposed new guidance for designing the highly selective catalysts for methanol synthesis. We investigated H2 and CO adsorption on a large number of metal and metal sulfide catalysts by first-principles calculations, and succeeded in clarifying the relationship between the metal species in the metal and metal sulfide catalysts and the products of the CO hydrogenation processes. Our results indicated that Co, Mo, Ru, Rh, Ir, and Pd sulfide catalysts selectively produce methanol, while Re and Os sulfide catalysts selectively produce hydrocarbons. The above results are in good agreement with the experimental results of Koizumi and co-workers. Moreover, we proposed that the Pd sulfide catalyst has the highest selectivity for methanol from the CO hydrogenation process. This result strongly supports the experimental results by Koizumi and co-workers. Moreover, we propose that the metal sulfide catalysts, which realize the bridge-site adsorption of the CO molecule on both the metal and sulfur atoms, have high selectivity for methanol. This proposed guidance for designing the highly selective metal sulfide catalysts for methanol may be useful for the experiments.

Editorial: Peptides as cancer therapeutics

Of the over one hundred anticancer drugs approved for patient use in the United States, the majority are small molecule cytotoxic drugs. Some are proteins such as interferon, interleukin-2, and monoclonal antibodies; only a handful are peptides. Gonadotropin releasing hormone analogue is an example of an anti-cancer peptide that has been used successfully as first line therapy for metastatic hormonal responsive prostate cancer, and it causes few side effects. It works by suppressing testosterone production which in turn inhibits prostate cancer growth. However, with few exceptions, most of the anticancer agents in use nowadays are rather non-specific and cause significant undesirable side effects on patients. There is accordingly a need for the development of new and effective cancer therapeutics. With the major advances in our basic understanding of oncogenesis and the amazing technologic development in recent years in genomics, proteomics, and drug discovery tools such as combinatorial chemistry, computational chemistry, and high throughput assays, there is genuine hope that more effective and less toxic anti-cancer drugs will be developed in the next decade or two. These drugs are likely to be target specific and less toxic. Although many of these drugs being developed are small molecules or proteins, several are peptides. This issue of Peptide Science highlights some of the recent developments in using peptides as cancer therapeutics. In the first article, Hamel describes the biochemical effects of a number of naturally occurring antimitotic peptides that target tubulin. These compounds are derived from plants, marine animals or microorganisms. They include phomopsins, ustiloxins, dolastatin, cemadotin, crytophysin, hemiasterlin, criamide, vitilevuamide, tubulysin, moroidin, and celogentin. Then, Froidevaux and Eberle outline the biochemistry of somatostatin and its five receptor subtypes. They summarize the literature on the use of unlabeled somatostatin analogues in the treatment of a number of different human cancers, and outline the development of radiolabeled somatostatin analogues as cancer targeting agents. This latter approach leads naturally into the subsequent paper by Lam and colleagues, where the rationale for the development of peptides-rather than monoclonal antibodies-as targeting agents is presented. The literature on using phage display or one-bead one-compound combinatorial peptide libraries to identify cell-surface targeting peptides is reviewed, and applications of these peptides to cancer targeting and cancer diagnostics are detailed. The final paper by Kaumaya and his colleagues surveys the literature on the use of peptides as cancer vaccines in patients. It also describes the ongoing research in the authors' laboratory relating to the use of co-stimulatory molecules and cytokines to augment immune responses. Peptides are likely to play a very important role in the treatment of cancer in the future, particularly in the areas of cell surface targeting and tumor peptide vaccines. As such, we hope readers will find this issue of Peptide Science both topical and stimulating.

Combinatorial computational chemistry approach to the design of cathode materials for a lithium secondary battery

Combinational chemistry is an efficient technique to find materials with novel properties by synthesizing and screening a large number of compounds in a short time. Recently, we introduced the concept of combinational approach into computational chemistry and proposed a novel approach, “combinatorial computational chemistry”. In the present study, we applied combinatorial computational chemistry to investigate the structural properties of lithium transition metal oxides, LiMO 2 (M=3d transitional metal), with a layered rocksalt structure. LiMO 2 is a promising material as positive electrodes in rechargeable lithium batteries. Density functional calculations on periodic models were performed to investigate the structural properties of LiCoO 2, LiNiO 2, and doped LiNiO 2, revealing that the poor charge–discharge cyclic reversibility of LiNiO 2 resulted from the large change in the structure due to the difference in the bond length between Ni 3+–O and Ni 4+–O. The analysis of the structural properties of Li 0.66Ni 0.5Me 0.5O 2 (Me=dopant) revealed that doping with Co decreased the change in the structure of LiNiO 2 during cycling. Doping of Ni with Al was also found to stabilize LiNiO 2.

Combinatorial computational chemistry approach to the design of methanol synthesis catalyst

Combinatorial chemistry is an efficient technique for the synthesis and screening of a large number of compounds. Recently, we introduced a concept of combinatorial chemistry to computational chemistry for catalyst design and proposed a new method called “combinatorial computational chemistry”. In the present study, we have applied our combinatorial computational chemistry approach to the design of methanol synthesis catalysts. Here, we investigated the formation energies of the intermediates during the methanol synthesis process on many metal catalysts, such as Cu, Ru, Rh, Pd, Ag, Re, Os, Pt, and Au, by using density functional calculations. We have also investigated the activity of the cationic species, such as Cu +, Ru +, Rh +, Pd +, Ag +, Re +, Os +, Pt +, and Au +, since it has been experimentally pointed out that the Cu + cation is an active center for the industrial Cu/ZnO/Al 2O 3 catalysts. Our calculation results confirm that the Cu + cation is an active catalyst for the methanol synthesis, which is in good agreement with the previous experimental results. Moreover, Ag + and Au + are suggested to be effective candidates of highly active catalysts for the methanol synthesis.

Combinatorial computational chemistry approach as a promising method for design of Fischer–Tropsch catalysts based on Fe and Co

The combinatorial computational chemistry approach was applied to design new types of catalysts, which can be used in the Fisher–Tropsh (FT) synthesis for the production of ecologically high-quality transportation fuels. For this purpose, the density functional theory (DFT) was used to investigate the CO adsorption on Fe- and Co-based multi-component catalysts. The energetic, electronic and structural properties of CO on the catalyst surfaces were calculated. It was found that Mn, Mo, and Zr could be used as additional elements in the Fe- and Co-based catalysts, since one cannot observe a degradation of the adsorption properties of the active sites as well as showing a high sulfur tolerance. For the Co-based catalyst, the same tendency is also found in the case of the Si promoter. The obtained results are in agreement with available experimental data that confirmed the validity of combinatorial computational chemistry approach.

Development of inhibitors for protein tyrosine kinases.

In the last 5 years, through combinatorial chemistry, high-throughput screening, computational chemistry, and traditional medicinal chemistry, numerous inhibitors for various protein tyrosine kinases (PTKs) have been developed. The majority of these compounds are small molecules that compete at the ATP binding site of the catalytic domain of the enzymes. Some compounds such as pseudosubstrate-based peptide inhibitor binds to the peptide/protein substrate site of the catalytic domain. Some inhibitors, primarily monoclonal antibodies, bind to the extracellular domain of receptor tyrosine kinases. Some of these inhibitors are highly potent and selective. Several are currently undergoing clinical trials for a number of diseases such as cancer.

Combinatorial computational chemistry approach to the design of deNO x catalysts

Combinatorial chemistry is an efficient technique for the synthesis and screening of a large number of compounds. Recently, we introduced the combinatorial approach to computational chemistry for catalyst design and proposed a new method called ‘combinatorial computational chemistry’. In the present study, we have applied this combinatorial computational chemistry approach to the design of deNO x catalysts. Various ion-exchanged ZSM-5 are candidates as catalysts for the removal of nitrogen oxides (NO x ) from exhaust gases in the presence of excess oxygen. Here, we describe the screening of the exchange cations in ion-exchanged ZSM-5 which are strong against poisons. We investigated the adsorption energies of NO and water on various ion-exchanged ZSM-5 catalysts. Cu +, Ag +, Au +, Fe 2+, Co 2+, Ni 2+, Pd 2+, Pt 2+, Cr 3+, Fe 3+, Ir 3+ and Tl 3+ were found to have high resistance to water molecules during the deNO x reaction.