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Proteolysis-targeting chimeras (PROTACs) are novel therapeutics for the treatment of human disease. They exploit the enormous potential of the E3 ligases, a class of proteins that mark a target protein for degradation via the ubiquitin-proteasome system. Despite the existence of several E3 ligase-related databases, the choice of the functioning ligase is limited to only 1.6% of those available, probably due to the fragmentary understanding of their structures and their known ligands; in fact, none of the existing databases report detailed studies covering their 3D structure or their pockets. Here, we report ELIOT (E3 LIgase pocketOme navigaTor), an accurate and complete platform containing the E3 ligase pocketome to enable navigation and selection of new E3 ligases and new ligands for the design of new PROTACs. All E3 ligase pockets were characterized with innovative 3D descriptors including their PROTAC-ability score, and similarity analyses between E3 pockets are presented. Tissue specificity and their degree of involvement in patients with specific cancer types are also annotated for each E3 ligase, enabling appropriate selection for the design of a PROTAC with improved specificity. All data are available at https://eliot.moldiscovery.com.

Specific microRNAs (miRs), including the "angio-miR-126" and the "inflamma-miR-146a-5p," have been proposed as biomarkers and even therapeutic targets of obesity-associated metabolic diseases. Physical activity, a key measure of prevention for obesity and its complications, is reported to influence the expression of these miRs. In this study, we investigate whether a physical activity program proven to improve metabolic parameters in obese patients can correct the circulating levels of these miRs. Plasma miR-126 and miR-146a-5p were measured in a cohort of obese patients (n = 31, 16F + 15M) before and after the 3-month physical activity program of the CURIAMO trial (registration number for clinical trials: ACTRN12611000255987) and in 37 lean controls (24F + 13M). miR-146a-5p, but not miR-126, was significantly increased in obese patients as compared with lean controls and decreased in approximately two-thirds of the participants post-intervention with a response that positively correlated with pre-intervention levels of this miR. Waist circumference, the inflammatory cytokine IL-8 and lipid parameters, principally total cholesterol, showed the strongest correlation with both the baseline levels and post-intervention correction of miR-146a-5p. Post-hoc analysis of experimental data supports the use of miR-146a-5p as a biomarker and predictor of the clinical response to physical activity in obese patients. Furthermore, miR-146a-5p expression was confirmed to increase together with that of the inflammatory genes TLR4, NF-κB, IL-6, and TNF-α in LPS-stimulated human mononuclear leukocytes. In conclusion, the inflamma-miR-146a-5p can serve as a personalized predictor of clinical outcome in obese patients entering physical activity weight-reduction programs. © 2018 IUBMB Life, 70(10):1012-1022, 2018.

The tectonic/stratigraphic interpretation of the Cambro–Ordovician rocks exposed in the south-westernmost sectors of the Sardinia island (i.e., belonging to the Bithia Formation or Unit) is still a hot point of discussion. A debated question is also the age of the metavolcanics interbedded in these rocks. In order to provide a clear answer to this problem, in this paper, we provide new U–Pb zircon dating and a petrographic–geochemical comparison of metavolcanics interbedded within the Bithia Formation with similar Sardinian Ordovician calc-alkaline rocks. The Bithia metavolcanics plot in the rhyodacite/rhyolite field. They show negative troughs for Nb and Ta, negative anomalies for Sr, P, and Ti, and positive peaks for Cs, Rb, K, Th, U, and Pb in the multielement diagram normalized to primitive mantle and significant LREE enrichment, marked negative Eu anomaly, and flat HREE patterns in the chondrite-normalized REE diagram. Owing to these geochemical features, the Bithia metavolcanics plot in the volcanic arc and active continental margin fields closely resembling Middle-Late Ordovician felsic metavolcanics from the Sarrabus, Gerrei, Goceano, and Mt. Grighini units. A new U–Pb zircon age of 462.1 ± 4.3 Ma yielded by a metavolcanic layer within the Bithia Formation at Capo Malfatano must be interpreted as the emplacement age. The CL and BSE images of zircon grains do not show any evidence of interaction with late hydrothermal fluids that could have caused Pb loss and rejuvenated ages, confirming the Middle-Late Ordovician age of the metavolcanics.

A multiclass method for screening and confirmatory analysis of antimicrobial residues in milk has been developed and validated. Sixty-two antibiotics belonging to ten different drug families (amphenicols, cephalosporins, lincosamides, macrolides, penicillin, pleuromutilins, quinolones, rifamycins, sulfonamides and tetracyclines) have been included. After the addition of an aqueous solution of EDTA, the milk samples were extracted twice with acetonitrile, evaporated and dissolved in ammonium acetate. After centrifugation, 10 mu l were analysed using LC-Q-Orbitrap operating in positive electrospray ionization mode. The method was validated in bovine milk in the range 2-150 mu gkg(-1) for all antibiotics; for four compounds with maximum residue limits higher than 100 mu gkg(-1), the validation interval has been extended until 333 mu gkg(-1). The estimated performance characteristics were satisfactory complying with the requirements of Commission Decision 2002/657/EC. Good accuracies were obtained also taking advantage from the versatility of the hybrid mass analyser. Identification criteria were achieved verifying the mass accuracy and ion ratio of two ions, including the pseudomolecular one, where possible. Finally, the developed procedure was applied to 13 real cases of suspect milk samples (microbiological assay) confirming the presence of one or more antibiotics, although frequently, the maximum residue limits were not exceeded. The availability of rapid multiclass confirmatory methods can avoid wastes of suspect, but compliant, raw milk samples. Copyright (c) 2016 John Wiley & Sons, Ltd.

Background and purpose An influx drug/proton antiporter of unknown structure has been functionally demonstrated at the blood-brain barrier. This transporter, which handles some psychoactive drugs like diphenhydramine, clonidine, oxycodone, nicotine and cocaine, could represent a new pharmacological target in drug addiction therapy. However, at present there are no known drugs/inhibitors that effectively inhibit/modulate this transporter in vivo. Experimental approach The FLAPpharm approach was used to establish a pharmacophore model for inhibitors of this transporter. The inhibitory potency of 44 selected compounds was determined against the specific substrate, [(3)H]-clonidine, in the human cerebral endothelial cell line hCMEC/D3 and ranked as good, medium, weak or non-inhibitor. Key results The pharmacophore model obtained was used as a template to screen xenobiotic and endogenous compounds from databases [Specs, Recon2, Human Metabolome Database (HMDB), human intestinal transporter database], and hypothetical candidates were tested in vitro to determine their inhibitory capacity with [(3)H]-clonidine. According to the transporter database, 80% of the proton antiporter inhibitor candidates could inhibit P-glycoprotein/MDR1/ABCB1 and specificity is improved by reducing inhibitor size/shape and increasing water solubility. Virtual screening results using HMDB and Recon2 for endogenous compounds appropriately scored tryptamine as an inhibitor. Conclusions and implications The pharmacophore model for the proton-antiporter inhibitors was a good predictor of known inhibitors and allowed us to identify new good inhibitors. This model marks a new step towards the discovery of this drug/proton antiporter and will be of great use for the discovery and design of potent inhibitors that could potentially help to assess and validate its pharmacological role in drug addiction in vivo.

The structural comparison of protein binding sites is increasingly important in drug design; identifying structurally similar sites can be useful for techniques such as drug repurposing, and also in a polypharmacological approach to deliberately affect multiple targets in a disease pathway, or to explain unwanted off-target effects. Once similar sites are identified, identifying local differences can aid in the design of selectivity. Such an approach moves away from the classical “one target one drug” approach and toward a wider systems biology paradigm. Here, we report a semiautomated approach, called BioGPS, that is based on the software FLAP which combines GRID Molecular Interactions Fields (MIFs) and pharmacophoric fingerprints. BioGPS comprises the automatic preparation of protein structure data, identification of binding sites, and subsequent comparison by aligning the sites and directly comparing the MIFs. Chemometric approaches are included to reduce the complexity of the resulting data on large datasets, enabling focus on the most relevant information. Individual site similarities can be analyzed in terms of their Pharmacophoric Interaction Field (PIF) similarity, and importantly the differences in their PIFs can be extracted. Here we describe the BioGPS approach, and demonstrate its applicability to rationalize off-target effects (ERα and SERCA), to classify protein families and explain polypharmacology (ABL1 kinase and NQO2), and to rationalize selectivity between subfamilies (MAP kinases p38α/ERK2 and PPARδ/PPARγ). The examples shown demonstrate a significant validation of the method and illustrate the effectiveness of the approach. Proteins 2015; 83:517–532. © 2015 Wiley Periodicals, Inc.

Drug discovery is a highly complex and costly process, and in recent years, the pharmaceutical industry has shifted from traditional to genomics- and proteomics-based drug research strategies. The identification of druggable target sites, promising hits, and high quality leads are crucial steps in the early stages of drug discovery projects. Pharmacokinetic (PK) and drug metabolism profiling to optimize bioavailability, clearance, and toxicity are increasingly important areas to prevent costly failures in preclinical and clinical studies. The integration of a wide variety of technologies and expertise in multidisciplinary research teams combining synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds to pass these hurdles is now commonplace, although there remain challenging areas. Molecular interaction fields (MIFs) are widely used in a range of applications to support the discovery teams, characterizing molecules according to their favorable interaction sites and therefore enabling predictions to be made about how molecules might interact. The utility of MIF-based in silico approaches in drug design is extremely broad, including approaches to support experimental design in hit-finding, lead-optimization, physicochemical property prediction and PK modeling, drug metabolism prediction, and toxicity. C � 2013 John Wiley & Sons, Ltd.

The metamorphic evolution of a granulitized eclogite from Punta de li Tulchi, NE Sardinia, Italy, reconstructed utilizing a combined microstructural (symplectitic, coronitic and kelyphytic features) and thermodynamic approach, involved a complex metamorphic history with equilibrium attained only at a domainal scale. Microstructural analysis and mineral zoning allow recognition of reactants and products involved in successive balanced mineral reactions. The P–T conditions at which each microstructure was formed are constrained by calculating isochemical phase diagrams (pseudosections) for the composition of effectively reacting domains. A pre-symplectite stage developed during prograde metamorphism under conditions ranging from 660–680 °C, 1.6–1.8 GPa to 660–700 °C at 1.7–2.1 GPa. Pseudosections calculated for subsequent clinopyroxene + plagioclase and orthopyroxene + plagioclase symplectitic coronae using the composition of effectively reacting microdomains suggest temperature in excess of 800 °C and pressures of 1.0–1.3 GPa. Modelling the development of later plagioclase + amphibole coronae around garnet during decompression yields conditions of 730–830 °C and 0.8–1.1 GPa. H2O (wt%) isomodes indicate that the granulitized eclogites were H2O-undersaturated at peak-P conditions and during most of the subsequent heating and decompression. This allowed the preservation of prograde garnet zoning in spite of the strong granulite facies overprint. The P–T evolution of Punta de li Tulchi granulitized eclogite is very similar in shape to that registered by other NE Sardinia retrogressed eclogites thus suggesting a common tectonic scenario for their evolution.

A statistical method to predict protein pK(a) has been developed by using the 3D structure of a protein and a database of 434 experimental protein pK(a) values. Each pK(a) in the database is associated with a fingerprint that describes the chemical environment around an ionizable residue. A computational tool, MoKaBio, has been developed to identify automatically ionizable residues in a protein, generate fingerprints that describe the chemical environment around such residues, and predict pK(a) from the experimental pK(a) values in the database by using a similarity metric. The method, which retrieved the pK(a) of 429 of the 434 ionizable sites in the database correctly, was crossvalidated by leave-one-out and yielded root mean square error (RMSE) = 0.95, a result that is superior to that obtained by using the Null Model (RMSE 1.07) and other well-established protein pK(a) prediction tools. This novel approach is suitable to rationalize protein pK(a) by comparing the region around the ionizable site with similar regions whose ionizable site pK(a) is known. The pK(a) of residues that have a unique environment not represented in the training set cannot be predicted accurately, however, the method offers the advantage of being trainable to increase its predictive power.

The El Arenal metagabbros preserve coronitic shells of orthopyroxene ± Fe-oxide around olivine, as well as three different types of symplectite consisting of amphibole + spinel, clinopyroxene + spinel and, more rarely, orthopyroxene + spinel. The textural features of the metagabbros can be explained by the breakdown of the olivine + plagioclase pair, producing orthopyroxene coronas and clinopyroxene + spinel symplectites, followed by the formation of amphibole + spinel symplectites, reflecting a decrease in temperature and, possibly, an increase in water activity with respect to the previous stage. The metagabbros underwent a complex P–T history consisting of an igneous stage followed by cooling in granulite, amphibolite and greenschist facies conditions. Although the P–T conditions of emplacement of the igneous protolith are still doubtful, the magmatic assemblage suggests that igneous crystallization occurred at a pressure lower than 6 kbar and at 900–1100 °C. Granulitic P–T conditions have been estimated at about 900 °C and 7–8 kbar combining conventional thermobarometry and pseudosection analysis. Pseudosection calculation has also shown that the formation of the amphibole + spinel symplectite could have been favoured by an increase in water activity during the amphibolite stage, as the temperature of formation of this symplectite strongly depends on aH2O (

The original GRID/PCA technique was adapted for the development of a tool potentially useful for the plan of a research strategy in rational enzyme design. The use of the MOVE directive of GRID made it possible to partially take into account protein flexibility, and the multivariate analysis was used as an instrument for focusing only on relevant information related to the differences in enzyme substrate selectivities. The comparison of two different penicillin G acylases, from Escherichia coli and from Providentia rettgeri, was used as a case study; these enzymes are very similar and their reported selectivities differ only for a couple of mutations around the active site. The "flexible" GRID/PCA method was able to correctly predict the observed selectivity differences caused not only by mutations of residues of the active site but also by long range effects on substrate selectivity due to sequence mutations on residues not directly involved in substrate recognition.

Fifty-nine xanthones (= 9H-xanthen-9-ones) of natural or synthetic origin were investigated for their inhibitory activity toward monoamine-oxidase A (MAO-A) and MAO-B. The compounds demonstrated reversible. time-independent activities, with selectivity toward MAO-A. The most active inhibitor had an IC50 of 40 nM. Electron absorption spectroscopy revealed the formation of a 1:1 charge-transfer complex between lumiflavine and xanthones. 3D-QSAR Studies according to the CoMFA/GOLPE procedure provided information on the relationship between steric and electrostatic fields and MAO-A inhibition. The ALMOND procedure yielded additional topographical information on structural factors favoring activity

A set of 40 heteroaromatic systems was multivariately characterized by 13 descriptors derived by GRID. From this data matrix a second generation of Principal Properties for heteroaromatics was derived. Such Principal Properties are suitable for designing series of molecules of biological interest containing heteroaromatic moieties.

An advanced variable selection procedure, called GOLPE, aimed at obtaining PLS regression models with the highest prediction ability is presented and illustrated with an application in 3D-QSAR. Key steps in the procedure are a preliminary variable selection by means of a D-optimal design in the loading space, and an iterative evaluation of the effects of individual variables on the model predictivity based on the validation of a number of reduced models, on variables combinations selected according to a FFD strategy. The procedure is successfully applied to a real 3D-QSAR case study: the results obtained by GOLPE are compared with those obtained by CoMFA and found to be in good agreement in terms of variable importance, but with a much higher prediction ability. Accordingly, the results encourage to think that it might be used within the CoMFA framework in the place of the present PLS version there, or in CoMFA-like studies on the structures generated by GRID probes.

Our combination of both kinetic and statistical (based on a refined version of the “principal-component analysis-self-modelling” method) fluorescence analysis (KSFA) has been applied to study the rotamerism of the trans isomer of 1-(2-naphthyl)-2-(6′-quinolyl)ethylene (2,6′-NQE). The effect of the excitation wavelength on the fluorescence spectra, quantum yield and decay profiles allowed the spectral and photophysical properties and the ground-state energy difference of the three rotamers of trans -2,6′-NQE to be obtained.

Improving the ADME profile of drug candidates is a critical step in lead optimization, and because pKa affects most ADME properties such as lipophilicity, solubility, and metabolism, it is extremely advantageous to predict pKa in order to guide the design of new compounds. However, accurately (

The standard deviation of prediction errors (SDEP) is used to evaluate and compare the predictive ability of some regression models, namely MLR, ACE and linear and non-linear PLS, the last being the best one. The parameter is determined by a cross-validation approach as an average of several runs obtained on forming groups in a random way. The variation in SDEP with the number of latent variables in PLS is also discussed.

A procedure called GOLPE is suggested in order to detect those variables which increase the predictivity of PLS models. The procedure is based on evaluating the predictive power of a number of PLS models built by different combinations of variables selected according to a factorial design strategy. Examples are given of the efficiency of this variable selection procedure, which shows how these predictive PLS models are better than those obtained by all variables and better than the corresponding ordinary regression models.

MDM2 and MDMX are oncogenic homologue proteins that regulate the activity and stability of p53, a tumor suppressor protein involved in more than 50% of human cancers. While the large body of experiments so far accumulated has validated MDM2 as a therapeutically important target for the development of anticancer drugs, it is only recently that MDMX has also become an attractive target for the treatment of tumor cells expressing wild type p53. The availability of structural information of the N-terminal domain of MDM2 in complex with p53-derived peptides and inhibitors, and the very recent disclosure of the crystal structure of the N-terminal domain of MDMX bound to a p53 peptide, offer an unprecedented opportunity to provide insight into the molecular basis of p53 recognition and the identification of discriminating features affecting the binding of the tumor suppressor protein at MDM2 and MDMX. By using coarse graining simulations, in this study we report the exploration of the conformational transitions featured in the pathway leading from the apo-MDM2 and apo-MDMX states to the p53-bound MDM2 and p53-bound MDMX states, respectively. The results have enabled us to identify a pool of diverse conformational states of the oncogenic proteins that affect the binding of p53 and the presence of conserved and non-conserved interactions along the conformational transition pathway that may be exploited in the design of selective and dual modulators of MDM2 and MDMX activity.

Given the three-dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape-based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The "druggability" of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations.

One of the most important physicochemical properties of a molecule is pKa. It is known that two parameters imperative in ADME profiling, solubility, and lipophilicity are governed by pKa, and receptor binding can be influenced by pKa. Because most drugs are ionized in physiological conditions, pKa is particularly relevant to medicinal chemistry. Despite the numerous advances in high-throughput measurements, in silico determination is still the fastest and cheapest way of obtaining pKa. This paper presents a new original computational method for pKa prediction of organic compounds. Descriptors were generated using the program GRID, and these descriptors are based on molecular interaction fields precomputed on a set of molecular fragments. The new method was developed, trained, and cross-validated by using a large and diverse data set of 24 617 pKa values. This paper presents the results for a class of 421 acidic nitrogen compounds (RMSE = 0.41, r2 = 0.97, q2 = 0.87) and for a class of 947 six-membered N-he...

A fast new algorithm (Fingerprints for Ligands And Proteins or FLAP) able to describe small molecules and protein structures using a common reference framework of four-point pharmacophore fingerprints and a molecular-cavity shape is described in detail. The procedure starts by using the GRID force field to calculate molecular interaction fields, which are then used to identify particular target locations where an energetic interaction with small molecular features would be very favorable. The target points thus calculated are then used by FLAP to build all possible four-point pharmacophores present in the given target site. A related approach can be applied to small molecules, using directly the GRID atom types to identify pharmacophoric features, and this complementary description of the target and ligand then leads to several novel applications. FLAP can be used for selectivity studies or similarity analyses in order to compare macromolecules without superposing them. Protein families can be compared and clustered into target classes, without bias from previous knowledge and without requiring protein superposition, alignment, or knowledge-based comparison. FLAP can be used effectively for ligand-based virtual screening and structure-based virtual screening, with the pharmacophore molecular recognition. Finally, the new method can calculate descriptors for chemometric analysis and can initiate a docking procedure. This paper presents the background to the new procedure and includes case studies illustrating several relevant applications of the new approach.

A Personal Foreword. Preface. List of Contributors. I Introduction. 1 The Basic Principles of GRID (Peter Goodford). 1.1 Introduction. 1.2 Philosophy and Objectives. 1.3 Priorities. 1.4 The GRID Method. 1.5 The GRID Force Field. 1.6 Nomenclature. 1.7 Calibrating the GRID Force Field. 1.8 The Output from GRID. 1.9 Conclusions. 2 Calculation and Application of Molecular Interaction Fields (Rebecca C. Wade). 2.1 Introduction. 2.2 Calculation of MIFs. 2.3 Selected Applications of MIFs. 2.4 Concluding Remarks and Outlook. II Pharmacodynamics. 3 Protein Selectivity Studies Using GRID-MIFs (Thomas Fox). 3.1 Introduction. 3.2 GRID Calculations and Chemometric Analysis. 3.3 Applications. 3.4 Discussion and Conclusion. 4 FLAP: 4-Point Pharmacophore Fingerprints from GRID (Francesca Perruccio, Jonathan S. Mason, Simone Sciabola, and Massimo Baroni). 4.1 Introduction. 4.2 FLAP Theory. 4.3 Docking. 4.4 Structure Based Virtual Screening (SBVS). 4.5 Ligand Based Virtual Screening (LBVS). 4.6 Protein Similarity. 4.7 TOPP (Triplets of Pharmacophoric Points). 4.8 Conclusions. 5 The Complexity of Molecular Interaction: Molecular Shape Fingerprints by the PathFinder Approach (Iain McLay, Mike Hann, Emanuele Carosati, Gabriele Cruciani, and Massimo Baroni). 5.1 Introduction. 5.2 Background. 5.3 The PathFinder Approach. 5.4 Examples. 5.5 Conclusions. 6 Alignment-independent Descriptors from Molecular Interaction Fields (Manuel Pastor). 6.1 Introduction. 6.2 GRIND. 6.3 How to Interpret a GRIND-based 3D QSAR Model. 6.4 GRIND Limitations and Problems. 6.5 Recent and Future Developments. 6.6 Conclusions. 7 3D-QSAR Using the GRID/GOLPE Approach (Wolfgang Sippl). 7.1 Introduction. 7.2 3D-QSAR Using the GRID/GOLPE Approach. 7.3 GRID/GOLPE Application Examples. 7.4 Conclusion. III Pharmacokinetics. 8 Use of MIF-based VolSurf Descriptors in Physicochemical and Pharmacokinetic Studies (Raimund Mannhold, Giuliano Berellini, Emanuele Carosati, and Paolo Benedetti). 8.1 ADME Properties and Their Prediction. 8.2 VolSurf Descriptors. 8.3 Application Examples. 8.4 Conclusion. 9 Molecular Interaction Fields in ADME and Safety (Giovanni Cianchetta, Yi Li, Robert Singleton, Meng Zhang, Marianne Wildgoose, David Rampe, Jiesheng Kang, and Roy J. Vaz). 9.1 Introduction. 9.2 GRID and MIFs. 9.3 Role of Pgp Efflux in the Absorption. 9.4 HERG Inhibition. 9.5 CYP 3A4 Inhibition. 9.6 Conclusions. 10 Progress in ADME Prediction Using GRID-Molecular Interaction Fields (Ismael Zamora, Marianne Ridderstr.m, Anna-Lena Ungell, Tommy Andersson, and Lovisa Afzelius). 10.1 Introduction: ADME Field in the Drug Discovery Process. 10.2 Absorption. 10.3 Distribution. 10.4 Metabolism. 10.5 Conclusions. 11 Rapid ADME Filters for Lead Discovery (Tudor I. Oprea, Paolo Benedetti, Giuliano Berellini, Marius Olah, Kim Fejgin, and Scott Boyer). 11.1 Introduction. 11.2 The Rule of Five (Ro5) as ADME Filter. 11.3 Molecular Interaction Fields (MIFs): VolSurf. 11.4 MIF-based ADME Models. 11.5 Clinical Pharmacokinetics (PK) and Toxicological (Tox) Datasets. 11.6 VolSurf in Clinical PK Data Modeling. 11.7 ChemGPS-VolSurf (GPSVS) in Clinical PK Property Modeling. 11.8 ADME Filters: GPSVS vs. Ro5. 11.9 PENGUINS: Ultrafast ADME Filter. 11.10 Integrated ADME and Binding Affinity Predictions. 11.11 Conclusions. 12 GRID-Derived Molecular Interaction Fields for Predicting the Site of Metabolism in Human Cytochromes (Gabriele Cruciani, Yasmin Aristei, Riccardo Vianello, and Massimo Baroni). 12.1 Introduction. 11.2 The Human Cytochromes P450. 12.3 CYPs Characterization using GRID Molecular Interaction Fields. 12.4 Description of the Method. 12.5 An Overview of the Most Significant Results. 12.6 Conclusions. 12.7 Software Package. Index. CD-ROM Information.

A set of a hundred aromatic substituents were multivariately characterized by nine descriptor variables taken from the literature. From the 9*100 data set were calculated four principal properties for the aromatic substituents as the four first dimensions in a principal components analysis, PCA. The first three principal properties were used to develop a strategy for selecting substituents from eight subgroups according to a factorial design.

The paper presents a procedure to obtain response surfaces with non-designed data. The method is based on PLS modelling of the expanded X-matrix followed by transformation of the PLS loadings into polynomial coefficients and detection of the co-ordinates of the best response within the experimental domain. The results are presented both graphically and numerically. The procedure is validated on an optimization study of the yield of an organic reaction.