Antimitotic agents are potential compounds for the treatment of breast cancer. Cytotoxicity is one of the parameters required for anticancer activity. A validated comparative molecular modeling study was performed on a set of phenylindole derivatives through R-group QSAR (RQSAR), regression-based and linear discriminant analysis (LDA)-based 2D QSAR studies and kernel-based partial least square (KPLS) analyses as well as CoMSIA 3D-QSAR study. Antiproliferative activities against two breast cancer cell lines (MDA-MB-231 and MCF7) were separately used as dependent variables. The RQSAR analysis highlighted different E-state indices and pharmacophoric requirements of important substitutions. The best 2D-QSAR model is established on the basis of three machine learning tools - MLR, SVM and ANN. The 2D-QSAR models depicted importance of different structural, physicochemical and topological descriptors. While RQSAR analyses demonstrated the fingerprint requirements of various substitutions, the KPLS analyses showed these requirements for the entire molecule. The CoMSIA model further refines these interpretations and reveals how subtle variations in these structures may influence biological activities. Observations of different modeling techniques complied with each other. The current QSAR study may be used to design potential antimitotic agents. It also demonstrates the utilities of different molecular modeling tools to elucidate the SAR. © 2015 Elsevier Ltd.

In this work, we performed atomistic simulations to study the structural properties of mixed self-assembled monolayers (SAM) of hydrophilic and hydrophobic alkylthiols, with two different chain lengths (C5 and C11), on gold nanoparticles (NPs) at three different arrangements, namely: random, patchy, and Janus domains. In particular, we report the effect of mixing of thiols with unequal carbon chain lengths (C5 and C11) at three different arrangements on the structural properties and hydration of SAMs. Our simulation study reveals that the arrangement of thiols having unequal carbon chains in mixed SAMs is a key parameter in deciding the hydrophilicity of the coated gold NPs. Thus, our findings suggest that the hydration of the SAMs-protected gold NPs is not only dependent on the molecular composition of the thiols, but also on the organization of their mixing. In addition, our results show that the bending of longer thiols, when these are mixed with shorter thiols, depends on the arrangement of thiols as well as the chemical nature of their terminal groups.

Reliable force field (FF) is a central issue in successful prediction of physical chemical properties via computer simulations. While Canongia Lopes-Padua (CL&P) FF provides good to excellent thermodynamics and structure of pure room-temperature ionic liquids (RTILs), it suffers from drastically and systematically underestimated ionic motion. This occurs due to neglected partial electron transfer from the anion to the cation, resulting in unphysically small simulated self-diffusion and conductivity and high shear viscosities. We report a systematic refinement of the CL&P FF for six pyrrolidinium-based RTILs (1-N-butyl-1-methyl-pyrrolidinium dicyanamide, triflate) bis(fluorosulfonyl)imide, bis(trifluoromethanesulfonyl)imide, tetrafluoroborate, chloride). The elaborated procedure accounts for specific cation anion interactions in the liquid phase. Once these interactions are described effectively, experimentally determined transport properties can be reproduced with an acceptable accuracy. Together with the original CL&P parameters, our force field fosters computational investigation of ionic liquids. In addition, the reported results shed more light on the chemical nature of cation anion binding in various families of RTILs.

<p>The shape of a buckybowl plays a fundamental role in the enhancement of fullerene recognition. Compounds whose structure possesses flaps at the rim of the bowl show an enhanced ability.</p>

Isolation of andrographolide from Andrographis paniculata, preparation of a library of derivatives via 1,3-dipolar cycloaddition of andrographolide with azomethine ylides generated from isatin derivatives or acenaphthoquinone and seconday a-amino acids, evaluation of the anticancer potential of the products, quantitative structure activity relationship studies and pharmacokinetic parameter determination have been described. 2D QSAR studies revaled that steric effects and van der Waals interactions play major roles in the determination of antiproliferative activity of these derivatives. 3D QSAR study predicted that the benzyl substitution at N20 position may be important for higher steric interaction. Pharmacokinetic studies with two most potent analogues revealed moderate chemical stability but poor aqueous solubility, metabolic stability and permeability with significant CYP3A4 inhibition. Keywords: 1,3-dipolar cycloaddition, 2D and 3D QSAR, Andrographolide, Anticancer, Dispiro-pyrrolidino/ pyrrolizidinooxindole, Pharmacokinetics. © 2015 Bentham Science Publishers.

Chagas' disease, which occurs particularly in South America is a human tropical parasitic disease, caused by Trypanosoma cruzi. A virtual screening in an in-house databank (SISTEMATX), of 469 Apocynaceae indole alkaloids, using models developed with fragment descriptors using Support Vector Machines (SVM) and Decision Trees (DT) were performed. A dataset 545 agrochemicals selected from ChEMBL database was used to generate both models and the prediction performance was tested using a small set of 44 alkaloids with the antitrypanosomal activity. From 469 Apocynaceae alkaloids, the SVM model selected, as actives, 5 similar alkaloids, from 2 species of the Aspidosperma genus (excelsum, marcgravianum), and the DT model selected 3 alkaloids from 3 species (gilbertii, nigracans, and subincanum) of the same genera from the SISTEMATX database. The values of Moriguchi octanol-water partition coefficient for these structures are between 2.3 to 5.3, and 5 alkaloids, passed the Lipinski alert index filter and Drug Like Score consensus (>0.7), which indicate that these compounds are good candidates to become a drug. These structures might be an interesting starting point for antitrypanosomal studies. The methodology, applying fragment descriptors and machine learning, was rapid and can be applied for virtual screening for bigger databases.

Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/ vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions. © Springer Science+Business Media New York 2015. All rights are reserved.

The impact activity cliffs have on drug discovery is double-edged. For instance, whereas medicinal chemists can take advantage of regions in chemical space rich in activity cliffs, QSAR practitioners need to escape from such regions. The influence of activity cliffs in medicinal chemistry applications is extensively documented. However, the 'dark side' of activity cliffs (i.e. their detrimental effect on the development of predictive machine learning algorithms) has been understudied. Similarly, limited amounts of work have been devoted to propose potential solutions to the drawbacks of activity cliffs in similarity-based approaches. In this review, the duality of activity cliffs in medicinal chemistry and computational approaches is addressed, with emphasis on the rationale and potential solutions for handling the 'ugly face' of activity cliffs.