Surface-Integral QSPR Models: Local Energy Properties

Self-Assembled Binary Nanoscale Systems: Multioutput Model with LFER-Covariance Perturbation Theory and an Experimental-Computational Study of NaGDC-DDAB Micelles

Studies of the self-aggregation of binary systems are of both theoretical and practical importance. They provide an opportunity to investigate the influence of the molecular structure of the hydrophobe on the nonideality of mixing. On the other hand, linear free energy relationship (LFER) models, such as Hansch's equations, may be used to predict the properties of chemical compounds such as drugs or surfactants. However, the task becomes more difficult once we want to predict simultaneaously the effect over multiple output properties of binary systems of perturbations under multiple input experimental boundary conditions (b(j)). As a consequence, we need computational chemistry or chemoinformatics models that may help us to predict different properties of the autoaggregation process of mixed surfactants under multiple conditions. In this work, we have developed the first model that combines perturbation theory (PT) and LFER ideas. The model uses as input covariance PT operators (CPTOs). CPTOs are calculated as the difference between covariance ΔCov((i)μ(k)) functions before and after multiple perturbations in the binary system. In turn, covariances calculated as the product of two Box-Jenkins operators (BJO) operators. BJOs are used to measure the deviation of the structure of different chemical compounds from a set of molecules measured under a given subset of experimental conditions. The best CPT-LFER model found predicted the effects of 25,000 perturbations over 9 different properties of binary systems. We also reported experimental studies of different experimental properties of the binary system formed by sodium glycodeoxycholate and didodecyldimethylammonium bromide (NaGDC-DDAB). Last, we used our CPT-LFER model to carry out a 1000 data point simulation of the properties of the NaGDC-DDAB system under different conditions not studied experimentally.

EMPIRE: a highly parallel semiempirical molecular orbital program: 2: periodic boundary conditions

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Graphical Abstract

Experimental and computational studies of fatty acid distribution networks

A new PT-LFER model is useful for predicting a distribution network in terms of specific fatty acid distribution.

Industrial applications of in silico ADMET

Quantitative structure activity relationship (QSAR) modeling has been in use for several decades now. One branch of it, in silico ADMET, became more and more important since the late 1990s as studies indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development. In this paper we describe some of the available methods and best practice for the different stages of the in silico model building process. We also describe some more recent developments, like automated model building and the prediction probability. Finally we will discuss the use of in silico ADMET for "big data" and the importance and possible further development of interpretable models.

The unrestricted local properties: application in nanoelectronics and for predicting radicals reactivity

The local electron affinity (EA(L)) and the local ionization energy (IE(L)) are successfully used for predicting properties of closed-shell species for drug design and for nanoelectronics. Here the respective unrestricted Hartree-Fock variants of EA(L) and IE(L), i.e., the unrestricted local electron affinity (UHF-EA(L)) and ionization energy (UHF-IE(L)), have been shown to be useful for predicting properties of open-shell species. UHF-EA(L) and UHF-IE(L) have been applied for explaining unique electronic properties of an exemplary nanomaterial carbon peapod. It is also demonstrated that UHF-EA(L) is useful for predicting and better understanding reactivity of radicals related to alkanes activation.

Development of in silico filters to predict activation of the pregnane X receptor (PXR) by structurally diverse drug-like molecules

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

QSAR studies using the parashift system

A novel way of describing molecules in terms of their surfaces and local properties at the surfaces is described. The use of these surfaces and properties to explain chemical reactivity and model simple molecular properties has already been demonstrated. This study reports an examination of the use of these descriptions of molecules to model a simple chemical interaction (complex formation) and a diverse set of mutagens. Both of these systems have been modelled successfully and the results are discussed.

Designing better drugs: predicting cytochrome P450 metabolism

Many 3D ligand-based and structure-based computational approaches have been used to predict, and thus help explain, the metabolism catalyzed by the enzymes of the cytochrome P450 superfamily (P450s). P450s are responsible for >90% of the metabolism of all drugs, so the computational prediction of metabolism can help to design out drug-drug interactions in the early phases of the drug discovery process. Computational methodologies have focused on a few P450s that are directly involved in drug metabolism. The recently derived crystal structures for human P450s enable better 3D modelling of these important metabolizing enzymes. Models derived for P450s have evolved from simple comparisons of known substrates to more-elaborate experiments that require considerable computer power involving 3D overlaps and docking experiments. These models help to explain and, more importantly, predict the involvement of P450s in the metabolism of specific compounds and guide the drug-design process.