Exfoliation and stabilization mechanism of graphene in carbon dioxide expanded organic solvents: molecular dynamics simulations.

CO2 expanded organic solvents possess significant advantages in liquid-phase exfoliation to obtain monolayer/few-layer graphene from graphite. Further insights into the mechanism of graphene exfoliation in such solvents are essential to explore liquid-phase dispersion of graphene as a more potent alternative to chemical vapor deposition. In this study, dynamic processes of exfoliation and stabilization of graphene in CO2-N,N-dimethylformamide (DMF), CO2-N-methylpyrrolidone (NMP), CO2-dimethyl sulfoxide (DMSO), and CO2-ethanol (EtOH) were investigated using molecular dynamics simulations. The origin of the effect of each solvent on graphene exfoliation was analyzed quantitatively through potential mean force simulations. It has been found that the organic solvent in a CO2 expanded solvent should be chosen with proper surface tension, and there exist two different graphene exfoliation processes in the effective solvents, which can be described as "burger dissociation" and "extrusion-taking away" processes, respectively. In the former process, a characteristic "super-burger-like" conformation with a semi-exfoliated structure was formed, which was the deciding factor to obtain high ratio of monolayer/few-layer graphene in dispersion product. A theoretical explanation has also been provided at the molecular level to the earlier experimental phenomena. A predicted simulation of the CO2-3,3'-iminobis(N,N-dimethylpropylamine) (DMPA) system is also calculated. This investigation helps to avoid incompatible CO2 expanded organic solvents employed in the experimental studies and provides theoretical clues to understand the mechanism of exfoliation and stabilization of graphene in such solvents.

Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016.

We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

Synthesis of Complex Druglike Molecules by the Use of Highly Functionalized Bench-Stable Organozinc Reagents.

The reactivity of a representative set of 17 organozinc pivalates with 18 polyfunctional druglike electrophiles (informers) in Negishi cross-coupling reactions was evaluated by high-throughput experimentation protocols. The high-fidelity scaleup of successful reactions in parallel enabled the isolation of sufficient material for biological testing, thus demonstrating the high value of these new solid zinc reagents in a drug-discovery setting and potentially for many other applications in chemistry. Principal component analysis (PCA) clearly defined the independent roles of the zincates and the informers toward druggable-space coverage.

Heterocyclic Regioisomer Enumeration (HREMS): A Cheminformatics Design Tool.

We report the development and implementation of a cheminformatics tool which aids in the design of compounds during exploratory chemistry and lead optimization. The Heterocyclic Regioisomer Enumeration and MDDR Search (HREMS) tool allows medicinal chemists to build greater structural diversity into their synthetic planning by enabling a systematic, automated enumeration of heterocyclic regioisomers of target structures. To help chemists overcome biases arising from past experience or synthetic accessibility, the HREMS tool further provides statistics on clinical testing for each enumerated regioisomer substructure using an automated search of a commercial database. Ready access to this type of information can help chemists make informed choices on the targets they will pursue being mindful of past experience with these structures in drug development. This tool and its components can be incorporated into other cheminformatics workflows to leverage their capabilities in triaging and in silico compound enumeration.

Very large virtual compound spaces: construction, storage and utility in drug discovery.

Recent activities in the construction, storage and exploration of very large virtual compound spaces are reviewed by this report. As expected, the systematic exploration of compound spaces at the highest resolution (individual atoms and bonds) is intrinsically intractable. By contrast, by staying within a finite number of reactions and a finite number of reactants or fragments, several virtual compound spaces have been constructed in a combinatorial fashion with sizes ranging from 10(11)11 to 10(20)20 compounds. Multiple search methods have been developed to perform searches (e.g. similarity, exact and substructure) into those compound spaces without the need for full enumeration. The up-front investment spent on synthetic feasibility during the construction of some of those virtual compound spaces enables a wider adoption by medicinal chemists to design and synthesize important compounds for drug discovery. Recent activities in the area of exploring virtual compound spaces via the evolutionary approach based on Genetic Algorithm also suggests a positive shift of focus from method development to workflow, integration and ease of use, all of which are required for this approach to be widely adopted by medicinal chemists.

Effects of MSCT enhanced scan image diagnosis on clinical outcome of patients after radical gastrectomy and its influence on misdiagnosis rate.

PURPOSE: To explore the effect of multi-slice spiral computed tomography (MSCT) enhanced scan image diagnosis on clinical outcome of patients after radical gastrectomy and its influence on misdiagnosis rate. METHODS: A total of 62 patients diagnosed with gastric cancer and undergoing radical gastrectomy were selected. All patients were reexamined 2-6 months after operation. Conventional CT and MSCT enhanced scan were performed for image diagnosis, and the results were compared with those of gastroscopic biopsy. Finally, the misdiagnosis rate, negative predictive value, positive predictive value, sensitivity and specificity of conventional CT and MSCT enhanced scan for postoperative recurrence were analyzed. RESULTS: According to the results of gastroscopic biopsy, there were 35 cases suspected of recurrence, and 27 cases without postoperative recurrence. The specificity and sensitivity of conventional CT and MSCT enhanced scan were 85.19% vs. 92.59%, and 65.71% vs. 92.16%, respectively. Both specificity and sensitivity of MSCT enhanced scan were higher than those of conventional CT, with statistically significant differences (p<0.05). MSCT enhanced scan had a lower misdiagnosis rate for postoperative recurrence than conventional CT (5.71% vs. 22.86%) (p<0.05). Moreover, the negative predictive value and positive predictive value of conventional CT and MSCT enhanced scan were 65.71% vs. 86.21%, and 85.19% vs. 93.94%, respectively. The results showed that MSCT enhanced scan had higher negative predictive value and positive predictive value for postoperative recurrence than conventional CT, with statistically significant differences (p<0.05). CONCLUSION: MSCT enhanced scan image diagnosis is of great significance for assessing the condition of disease, determining the recurrent foci after radical gastrectomy, and developing the subsequent therapeutic regimen.

Combinatorial library-based design with Basis Products.

Uncovering useful lead compounds from a vast virtual library of synthesizable compounds continues to be of tremendous interest to pharmaceutical researchers. Here we present the concept of Basis Products (BPs), a new and broadly applicable method for achieving efficient selections from a combinatorial library. By definition, Basis Products are a strategically selected subset of compounds from a potentially very large combinatorial library, and any compound in a combinatorial library can represented by its BPs. In this article we will show how to use BP docking scores to find the top compounds of a combinatorial library. Compared with the brute-force docking of an entire virtual library, docking with BPs are much more efficient because of the substantial size reduction, saving both time and resources. We will also demonstrate how BPs can be used for property-based combinatorial library designs. Furthermore, BPs can also be considered as fragments carrying chemistry knowledge, hence they can potentially be used in combination with any fragment-based design method. Therefore, BPs can be used to integrate combinatorial design with structure-based design and/or fragment-based design. Other potential applications of BPs include lead hopping and consensus core building, which we will describe briefly as well in this report.

Metal- and Acid-Free C-H Formylation of Nitrogen Heterocycles: Using Trioxane as an Aldehyde Equivalent Enabled by an Organic-Soluble Oxidant.

A metal-free, innate, and practical C-H formylation of nitrogen heterocycles using trioxane as a formyl equivalent is reported. This reaction provides a mild and robust method for modifying medicinally relevant heterocycles with an aldehyde handle. The use of an organic soluble oxidant, tetrabutylammonium persulfate, is critical in promoting the desired coupling.

Computational prediction of chemical reactions: current status and outlook.

Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities.

Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS.

Understanding the practical limitations of chemical reactions is critically important for efficiently planning the synthesis of compounds in pharmaceutical, agrochemical, and specialty chemical research and development. However, literature reports of the scope of new reactions are often cursory and biased toward successful results, severely limiting the ability to predict reaction outcomes for untested substrates. We herein illustrate strategies for carrying out large-scale surveys of chemical reactivity by using a material-sparing nanomole-scale automated synthesis platform with greatly expanded synthetic scope combined with ultrahigh-throughput matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS).

Structure-based design and synthesis of (5-arylamino-2H-pyrazol-3-yl)-biphenyl-2',4'-diols as novel and potent human CHK1 inhibitors.

The cocrystal structure of a library hit was used to design a novel series of CHK1 inhibitors. The new series retained the critical hydrogen-bonding groups of the resorcinol moiety for binding but lacked the phenolic anilide moiety. The newly designed compounds exhibited similar enzymatic activity, while demonstrating increased cellular potency. Compound 10c, showing no single agent effect, potentiated the antiproliferative effect of Gemcitabine in both prostate and breast cancer cell lines.

Local Recurrence after Radiofrequency Ablation of Hepatocellular Carcinoma: Treatment Choice and Outcome.

BACKGROUND/AIMS: Radiofrequency ablation (RFA) has been proven effective for treating small hepatocellular carcinoma (HCC) nodules. However, post-RFA local recurrence is a major factor limiting prognosis. Up to now, there is no consensus on a standardized treatment strategy for these local recurrences. The aim of this study is to evaluate the outcomes of salvage treatments for RFA-related local recurrence. METHODS: From May 2008 to June 2013, a total of 112 patients with HCC were detected with local recurrence after RFA. Among them, 94 patients received sequential treatments in our hospital, including salvage resection (SR) (n = 24), salvage liver transplantation (n = 2), repeated RFA (n = 62), and transarterial chemoembolization (TACE) (n = 6). We evaluated the treatment outcomes of patients by salvage surgery (SS), RFA, and TACE. RESULTS: The median follow-up time was 32 months. After treatment, local recurrence was eradicated in 82 of 94 patients (87.2%). The complete response (CR) rate in the RFA group was 90.3% (56/62), while it was 100% (26/26) in the SS group (P = 0.175) and 0% (0/6) in the TACE group. When analysis confined to patients with CR, the 1- and 3-year disease-free survival (DFS) rates were 57.7 and 20.2% in the SS group, and 41.7 and 28.6% in the RFA group, respectively (P = 0.640). The 1- and 3-year overall survival (OS) rates were 93.3 and 69.1% in the SS group, and 78.6 and 57.5% in the RFA group, respectively (P = 0.251). CONCLUSION: Repeated RFA is the first treatment choice for patients with post-RFA local recurrence. SS should be considered when RFA failed or is inapplicable.

Derivation of class II force fields. VIII. Derivation of a general quantum mechanical force field for organic compounds.

A class II valence force field covering a broad range of organic molecules has been derived employing ab initio quantum mechanical "observables." The procedure includes selecting representative molecules and molecular structures, and systematically sampling their energy surfaces as described by energies and energy first and second derivatives with respect to molecular deformations. In this article the procedure for fitting the force field parameters to these energies and energy derivatives is briefly reviewed. The application of the methodology to the derivation of a class II quantum mechanical force field (QMFF) for 32 organic functional groups is then described. A training set of 400 molecules spanning the 32 functional groups was used to parameterize the force field. The molecular families comprising the functional groups and, within each family, the torsional angles used to sample different conformers, are described. The number of stationary points (equilibria and transition states) for these molecules is given for each functional group. This set contains 1324 stationary structures, with 718 minimum energy structures and 606 transition states. The quality of the fit to the quantum data is gauged based on the deviations between the ab initio and force field energies and energy derivatives. The accuracy with which the QMFF reproduces the ab initio molecular bond lengths, bond angles, torsional angles, vibrational frequencies, and conformational energies is then given for each functional group. Consistently good accuracy is found for these computed properties for the various types of molecules. This demonstrates that the methodology is broadly applicable for the derivation of force field parameters across widely differing types of molecular structures. Copyright 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1782-1800, 2001

Mining gene-centric relationships from literature: the roles of gene mutation and gene expression in supporting drug discovery.

Identifying drug target candidates is an important task for early development throughout the drug discovery process. This process is supported by the development of new high-throughput technologies that enable better understanding of disease mechanism. It becomes critical to facilitate effective analysis of the large amount of biological data. However, with much of the biological knowledge represented in the literature in the form of natural text, analysis and interpretation of high-throughput data has not reached its potential effectiveness. In this paper, we describe our solution in employing text mining as a technique in finding scientific information for target and biomarker discovery from the biomedical literature. Our approach utilises natural language processing techniques to capture linguistic patterns for the extraction of biological knowledge from text. Additionally, we discuss how the extracted knowledge is used for the analysis of biological data such as next-generation sequencing and gene expression data.

Introducing Bayesian thinking to high-throughput screening for false-negative rate estimation.

High-throughput screening (HTS) has been widely used to identify active compounds (hits) that bind to biological targets. Because of cost concerns, the comprehensive screening of millions of compounds is typically conducted without replication. Real hits that fail to exhibit measurable activity in the primary screen due to random experimental errors will be lost as false-negatives. Conceivably, the projected false-negative rate is a parameter that reflects screening quality. Furthermore, it can be used to guide the selection of optimal numbers of compounds for hit confirmation. Therefore, a method that predicts false-negative rates from the primary screening data is extremely valuable. In this article, we describe the implementation of a pilot screen on a representative fraction (1%) of the screening library in order to obtain information about assay variability as well as a preliminary hit activity distribution profile. Using this training data set, we then developed an algorithm based on Bayesian logic and Monte Carlo simulation to estimate the number of true active compounds and potential missed hits from the full library screen. We have applied this strategy to five screening projects. The results demonstrate that this method produces useful predictions on the numbers of false negatives.

Pfizer Global Virtual Library (PGVL): a chemistry design tool powered by experimentally validated parallel synthesis information.

An unprecedented amount of parallel synthesis information was accumulated within Pfizer over the past 12 years. This information was captured by an informatics tool known as PGVL (Pfizer Global Virtual Library). PGVL was used for many aspects of drug discovery including automated reactant mining and reaction product formation to build a synthetically feasible virtual compound collection. In this report, PGVL is discussed in detail. The chemistry information within PGVL has been used to extract synthesis and design information using an intuitive desktop Graphic User Interface, PGVL Hub. Several real-case examples of PGVL are also presented.

Design of targeted libraries against the human Chk1 kinase using PGVL Hub.

PGVL Hub is a Pfizer internal desktop tool for chemical library and singleton design. In this chapter, we give a short introduction to PGVL Hub, the core workflow it supports, and the rich design capabilities it provides. By re-creating two legacy targeted libraries against the human checkpoint kinase 1 (Chk1) as a showcase, we illustrate how PGVL Hub could be used to help library designers carry out the steps in library design and realize design objectives such as SAR expansion and improvement in both kinase selectivity and compound aqueous solubility. Finally we share several tips about library design and usage of PGVL Hub.

LEAP into the Pfizer Global Virtual Library (PGVL) space: creation of readily synthesizable design ideas automatically.

Pfizer Global Virtual Library (PGVL) of 10(13) readily synthesizable molecules offers a tremendous opportunity for lead optimization and scaffold hopping in drug discovery projects. However, mining into a chemical space of this size presents a challenge for the concomitant design informatics due to the fact that standard molecular similarity searches against a collection of explicit molecules cannot be utilized, since no chemical information system could create and manage more than 10(8) explicit molecules. Nevertheless, by accepting a tolerable level of false negatives in search results, we were able to bypass the need for full 10(13) enumeration and enabled the efficient similarity search and retrieval into this huge chemical space for practical usage by medicinal chemists. In this report, two search methods (LEAP1 and LEAP2) are presented. The first method uses PGVL reaction knowledge to disassemble the incoming search query molecule into a set of reactants and then uses reactant-level similarities into actual available starting materials to focus on a much smaller sub-region of the full virtual library compound space. This sub-region is then explicitly enumerated and searched via a standard similarity method using the original query molecule. The second method uses a fuzzy mapping onto candidate reactions and does not require exact disassembly of the incoming query molecule. Instead Basis Products (or capped reactants) are mapped into the query molecule and the resultant asymmetric similarity scores are used to prioritize the corresponding reactions and reactant sets. All sets of Basis Products are inherently indexed to specific reactions and specific starting materials. This again allows focusing on a much smaller sub-region for explicit enumeration and subsequent standard product-level similarity search. A set of validation studies were conducted. The results have shown that the level of false negatives for the disassembly-based method is acceptable when the query molecule can be recognized for exact disassembly, and the fuzzy reaction mapping method based on Basis Products has an even better performance in terms of lower false-negative rate because it is not limited by the requirement that the query molecule needs to be recognized by any disassembly algorithm. Both search methods have been implemented and accessed through a powerful desktop molecular design tool (see ref. (33) for details). The chapter will end with a comparison of published search methods against large virtual chemical space.