Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure-Activity Relationships through Web Applications.

The increasing use of information technology in the discovery of new molecular entities encourages the use of modern molecular-modeling tools to help teach important concepts of drug design to chemistry and pharmacy undergraduate students. In particular, statistical models such as quantitative structure-activity relationships (QSAR)-often as its 3D QSAR variant-are commonly used in the development and optimization of a leading compound. We describe how these drug discovery methods can be taught and learned by means of free and open-source web applications, specifically the online platform www.3d-qsar.com. This new suite of web applications has been integrated into a drug design teaching course, one that provides both theoretical and practical perspectives. We include the teaching protocol by which pharmaceutical biotechnology master students at Pharmacy Faculty of Sapienza Rome University are introduced to drug design. Starting with a choice among recent articles describing the potencies of a series of molecules tested against a biological target, each student is expected to build a 3D QSAR ligand-based model from their chosen publication, proceeding as follows: creating the initial data set (Py-MolEdit); generating the global minimum conformations (Py-ConfSearch); proposing a promising mutual alignment (Py-Align); and finally, building, and optimizing a robust 3D QSAR models (Py-CoMFA). These student activities also help validate these new molecular modeling tools, especially for their usability by inexperienced hands. To more fully demonstrate the effectiveness of this protocol and its tools, we include the work performed by four of these students (four of the coauthors), detailing the satisfactory 3D QSAR models they obtained. Such scientifically complete experiences by undergraduates, made possible by the efficiency of the 3D QSAR methodology, provide exposure to computational tools in the same spirit as traditional laboratory exercises. With the obsolescence of the classic Comparative Molecular Field Analysis Sybyl host, the 3dqsar web portal offers one of the few available means of performing this well-established 3D QSAR method.

Hoarding disorder and the legal system: A comparative analysis of South African and Dutch law.

Hoarding is an internationally recognised disability. Those who suffer from hoarding behaviour can be comfortably brought within the definition of disability found in the Convention on the Rights of Persons with Disabilities and should be provided with "reasonable accommodation" where doing so does not place an unjustified burden on others. However, hoarding also poses a threat to public health, and hoarders' behaviour may infringe on the rights of their neighbours and landlords. Thus, through their behaviour, hoarders may ultimately come into conflict with various areas of law, including neighbour law, housing law as well as administrative law. This article examines how hoarding may be addressed by the law in both South Africa and the Netherlands. It seeks to answer to what extent hoarders are provided with "reasonable accommodation" when their behaviour brings them into conflict of the law in these two jurisdictions. It also takes cognisance of the need to balance the provision of "reasonable accommodation" with the rights of neighbours and landlords. Finally, it seeks to assess which of the two jurisdictions provides the most balanced approach to handling hoarding, in light of the need for therapeutic jurisprudence.

Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities.

The possible applicability of the new template CoMFA methodology to the prediction of unknown biological affinities was explored. For twelve selected targets, all ChEMBL binding affinities were used as training and/or prediction sets, making these 3D-QSAR models the most structurally diverse and among the largest ever. For six of the targets, X-ray crystallographic structures provided the aligned templates required as input (BACE, cdk1, chk2, carbonic anhydrase-II, factor Xa, PTP1B). For all targets including the other six (hERG, cyp3A4 binding, endocrine receptor, COX2, D2, and GABAa), six modeling protocols applied to only three familiar ligands provided six alternate sets of aligned templates. The statistical qualities of the six or seven models thus resulting for each individual target were remarkably similar. Also, perhaps unexpectedly, the standard deviations of the errors of cross-validation predictions accompanying model derivations were indistinguishable from the standard deviations of the errors of truly prospective predictions. These standard deviations of prediction ranged from 0.70 to 1.14 log units and averaged 0.89 (8x in concentration units) over the twelve targets, representing an average reduction of almost 50% in uncertainty, compared to the null hypothesis of "predicting" an unknown affinity to be the average of known affinities. These errors of prediction are similar to those from Tanimoto coefficients of fragment occurrence frequencies, the predominant approach to side effect prediction, which template CoMFA can augment by identifying additional active structural classes, by improving Tanimoto-only predictions, by yielding quantitative predictions of potency, and by providing interpretable guidance for avoiding or enhancing any specific target response.

Template CoMFA applied to 116 biological targets.

Statistically acceptable 3D-QSAR models were effortlessly obtained, by application of automatic template CoMFA, for the vast majority, arguably more than 95%, of 116 biological targets. Among these targets, 76 were structure-based, pooling multiple templates (as protein-bound conformation of ligands) and training sets into a single model, and the other 40 were ligand-based, with a low-energy conformation of an exemplar training set structure becoming the single template. Criteria proposed for statistical acceptability are a leave-one-out q(2) > 0.4, or a standard error of leave-one-out prediction <1.0, or a ratio of best-fit r(2) to count of PLS components >0.2. The structure-based 3D-QSAR models provide direct visual comparisons of SAR-derived 3D-QSAR contours with cavity surfaces.

Challenging the gold standard for 3D-QSAR: template CoMFA versus X-ray alignment.

X-ray-based alignments of bioactive compounds are commonly used to correlate structural changes with changes in potencies, ultimately leading to three-dimensional quantitative structure-activity relationships such as CoMFA or CoMSIA models that can provide further guidance for the design of new compounds. We have analyzed data sets where the alignment of the compounds is entirely based on experimentally derived ligand poses from X-ray-crystallography. We developed CoMFA and CoMSIA models from these X-ray-determined receptor-bound conformations and compared the results with models generated from ligand-centric Template CoMFA, finding that the fluctuations in the positions and conformations of compounds dominate X-ray-based alignments can yield poorer predictions than those from the self-consistent template CoMFA alignments. Also, when there exist multiple different binding modes, structural interpretation in terms of binding site constraints can often be simpler with template-based alignments than with X-ray-based alignments.

Template CoMFA: the 3D-QSAR Grail?

Template CoMFA, a novel alignment methodology for training or test set structures in 3D-QSAR, is introduced. Its two most significant advantages are its complete automation and its ability to derive a single combined model from multiple structural series affecting a biological target. Its only two inputs are one or more "template" structures having 3D coordinates that share some Cartesian space, as may result from X-ray crystallography or pharmacophoric hypothesis, and one or more connectivity-only SAR tables associated with a common target. Template CoMFA also overcomes the major disadvantages of both existing 3D-QSAR alignment methodologies, specifically the tedium and subjectivity of familiar ad hoc approaches, and the awkwardness, occasional physicochemical heresies, and structural scope limitations of the purely topomer approach. The template CoMFA algorithms are described, and two of its application classes are presented. The first class, general models of binding to factor Xa and P38 map kinase, uses crystallographic structures as templates, with the encouraging result that the statistical qualities of each of these two combined models are equivalent to those of their constituent individual series models. The second, 15 data sets originally collected for validation of topomer CoMFA, with arbitrary structures as templates, confirms that the modeling power of template CoMFA resembles that of its predecessors.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Electrochemical nanostructuring of n-GaAs photoelectrodes.

Methods to simultaneously optimize carrier collection and light in-coupling in semiconductors are important for developing low-cost, high-efficiency photovoltaics and photoelectrodes. We anodically etched nanostructures into planar (100) n-GaAs wafers with different bulk minority carrier diffusion lengths L(D). The structures were varied by changing the anodization parameters. A ferrocene/ferrocenium electrolyte provided a conformal rectifying contact to the anodized n-GaAs and enabled the measurement of carrier generation and collection as a function of nanostructure geometry and L(D). Internal quantum efficiency Φ(int) of photoelectrodes varied with nanostructure geometry and L(D). External quantum efficiency Φ(ext) also depended on the reflectance of the nanostructured GaAs-electrolyte interface. Reflectance was minimized using anodization current densities of 100-150 mA cm(-2), which etched subwavelength trigonal prismatic nanostructures ~400 nm in width at their base. For Si-doped n-GaAs with L(D) = 170 nm, peak Φ(ext) of ~75% and Φ(int) of ~85% was achieved using J(anod) = 150 mA cm(-2). The control of both surface nanostructure (to minimize reflection) and pore depth and spacing (to optimize 3D carrier collection) via two-step anodization yielded photoelectrodes with peak Φ(ext) of ~85% and peak Φ(int) of ~95% for Te-doped n-GaAs with a bulk L(D) of only 420 nm. The measured short-circuit current densities for the nanostructured photoelectrodes were up to 2.5 times that of planar controls, demonstrating that appropriate nanostructuring significantly improves carrier collection even for direct bandgap materials with large absorption coefficients like GaAs.

R-group template CoMFA combines benefits of "ad hoc" and topomer alignments using 3D-QSAR for lead optimization.

Template CoMFA methodologies extend topomer CoMFA by allowing user-designated templates, for example the experimental receptor-bound conformation of a prototypical ligand, to help determine the alignment of training and test set structures for 3D-QSAR. The algorithms that generate its new structural modality, template-constrained topomers, are described. Template CoMFA's resolution of certain topomer CoMFA concerns, by providing user control of topological consistency and structural acceptability, is demonstrated for sixteen 3D-QSAR training sets, in particular the Selwood dataset.

Efficient n-GaAs photoelectrodes grown by close-spaced vapor transport from a solid source.

n-GaAs films were grown epitaxially on n(+)-GaAs substrates by a close-spaced vapor transport method and their photoelectrochemical energy conversion properties studied. Under 100 mW cm(-2) of ELH solar simulation, conversion efficiencies up to 9.3% for CSVT n-GaAs photoanodes were measured in an unoptimized ferrocene/ferrocenium test cell. This value was significantly higher than the 5.7% measured for similarly doped commercial n-GaAs wafers. Spectral response experiments showed that the higher performance of CSVT n-GaAs films relative to the commercial wafers was due to longer minority carrier diffusion lengths (L(D)), up to 1,020 nm in the CSVT films compared to 260 nm in the commercial n-GaAs wafers. Routes to improve the performance of CSVT GaAs and the implications of these results for the development of scalable GaAs-based solar energy conversion devices are discussed.

The inevitable QSAR renaissance.

QSAR approaches, including recent advances in 3D-QSAR, are advantageous during the lead optimization phase of drug discovery and complementary with bioinformatics and growing data accessibility. Hints for future QSAR practitioners are also offered.

Rethinking 3D-QSAR.

The average error of pIC50 prediction reported for 140 structures in make-and-test applications of topomer CoMFA by four discovery organizations is 0.5. This remarkable accuracy can be understood to result from a topomer pose's goal of generating field differences only at lattice intersections adjacent to intended structural change.

Tautomers and topomers: challenging the uncertainties of direct physicochemical modeling.

To address the goal of improved discovery decision making, the uncertainties of physicochemical modeling, as exemplified by tautomer identification, are contrasted with methods focused exclusively on the sole experimental system variable, the changes in ligand structures, as exemplified by topomers.

Virtual screening for R-groups, including predicted pIC50 contributions, within large structural databases, using Topomer CoMFA.

Multiple R-groups (monovalent fragments) are implicitly accessible within most of the molecular structures that populate large structural databases. R-group searching would desirably consider pIC50 contribution forecasts as well as ligand similarities or docking scores. However, R-group searching, with or without pIC50 forecasts, is currently not practical. The most prevalent and reliable source of pIC50 predictions, existing 3D-QSAR approaches, is also difficult and somewhat subjective. Yet in 25 of 25 trials on data sets on which a field-based 3D-QSAR treatment had already succeeded, substitution of objective (canonically generated) topomer poses for the original structure-guided manual alignments produced acceptable 3D-QSAR models, on average having almost equivalent statistical quality to the published models, and with negligible effort. Their overall pIC50 prediction error is 0.805, calculated as the average over these 25 topomer CoMFA models in the standard deviations of pIC50 predictions, derived from the 1109 possible "leave-out-one-R-group" (LOORG) pIC50 contributions. (This novel LOORG protocol provides a more realistic and stringent test of prediction accuracy than the customary "leave-out-one-compound" LOO approach.) The associated average predictive r(2) of 0.495 indicates a pIC50 prediction accuracy roughly halfway between perfect and useless. To assess the ability of topomer-CoMFA based virtual screening to identify "highly active" R-groups, a Receiver Operating Curve (ROC) approach was adopted. Using, as the binary criterion for a "highly active" R-group, a predicted pIC50 greater than the top 25% of the observed pIC50 range, the ROC area averaged across the 25 topomer CoMFA models is 0.729. Conventionally interpreted, the odds that a "highly active" R-group will indeed confer such a high pIC50 are 0.729/(1-0.729) or almost 3 to 1. To confirm that virtual screening within large collections of realized structures would provide a useful quantity and variety of R-group suggestions, combining shape similarity with the "highly active" pIC50, the 50 searches provided by these 25 models were applied to 2.2 million structurally distinct R-group candidates among 2.0 million structures within a ZINC database, identifying an average of 5705 R-groups per search, with the highest predicted pIC50 combination averaging 1.6 log units greater than the highest reported pIC50s.

Quantitative Series Enrichment Analysis (QSEA): a novel procedure for 3D-QSAR analysis.

A novel procedure is proposed for 3D-QSAR analysis. The composition of 16 published QSAR datasets has been examined using Quantitative Series Enrichment Analysis (QSEA). The procedure is based on topomer technologies. A heatmap display in combination with topomer CoMFA and a novel series trajectory analysis revealed critical information for the assembly of structures into meaningful series. Global and local centroid structures can be determined from a similarity distance matrix and build the origins for stepwise model building by increasing the similarity radius around the centroid nucleus. The results indicate that the new procedure allows determination of whether compounds belong to an emerging structure-activity relationship and which compounds can be predicted within reliable limits.

AllChem: generating and searching 10(20) synthetically accessible structures.

AllChem is a system that is intended to make practical the generation and searching of an unprecedentedly vast number ( approximately 10(20)) of synthetically accessible and medicinally relevant structures. Also, by providing possible synthetic routes to a structure along with its design rationale, AllChem encourages simultaneous consideration of both costs and benefits during each lead discovery and optimization decision, thereby promising to be effective with synthetic chemists among its primary users. AllChem is still under intensive development so the following initial description necessarily has more the character of an interim progress report than of a finished research publication.

Pushing the boundaries of 3D-QSAR.

Based primarily on further studies of a collection of eleven publications reporting fifteen successful 3D-QSAR relations, several phenomena are preliminarily described. The RMS error of 133 ligand binding energy predictions based on these successful 3D-QSARs is 0.75 kcal/mole, which compares favorably to the prediction accuracies of approaches that include the receptor. A similar result is obtained when topomer alignments are substituted for those published, with seemingly profound implications for the future of 3D-QSAR. The "alignment-averaged" molecular properties, log P and molar refractivity, have very little correlative power for these data sets, either alone or in combination with the 3D-QSAR field descriptors. The q (2 )metric for the number of PLS components necessarily tends to discard any unique or unconfirmed SAR information. Large drops in q (2) are thus to be expected whenever such unique information is first encountered. Predictive r (2) values from an exploratory new "series trajectory" analysis of these 3D-QSAR though highly variable do not differ much from their q (2) values, a phenomenon that seems to encourage prediction even when there are so few structures underlying a 3D-QSAR so that almost all information is unique.