Cheminfostream

MSDsite: The technology used in database searching and retrieval for the analysis and viewing of bound ligands and active sites

Kim Henrick, the Macromolecular Structure Database (MSD) group leader at the European Bioinformatics Institute, will present the following seminar at the eCheminfo 2004 Web conference (http://echeminfo.com), 8-19 November 2004:

MSDsite: behind the scene: The technology used in database searching and retrieval for the analysis and viewing of bound ligands and active sites

Adel Golovin, Dimitris Dimitropoulos, Tom Oldfield, and Kim Henrick, EMBL Outstation, The European Bioinformatics Institute, Welcome Trust, Genome Campus, Hinxton, Cambridge CB10 1SD, UK

The three-dimensional environments of ligand binding sites have been derived from the parsing and loading of the PDB entries into a relational database. We will introduce the web-based query system, MSDsite (http://www.ebi.ac.uk/msd-srv/msdsite) and demonstrate the technologies used. Non-trivial textual queries are facilitated by use of a graphical query interface where search attributes can be specified with dialog boxes to build complex queries. The interface was built with a biological content in mind and Ligand searching requires specific tasks that can't be resolved using simple basic SQL relational operators such as join or merge. For fast executable in Oracle query generation a web application server has been developed that contains java classes that composes complex SQL queries andprovide calculations on the dataset. In addition these classes design the SQL to enforce the correct use of indexes, and apply query hints. The SQL queries are also designed to use the relational algebra operation 'INTERSECT', which allows execution within the Oracle RDBMS in parallel without nesting and is faster than self joins for those cases where the result set is many times less than the size of the table queried. The approach used is based on a star architecture query where the ligand is central and interactions to the environment residues fan out. This design is used because it results in an algorithm of order 'N' with regard to the number of environment residues and is therefore scalable for complex active sites. The levels of optimisation developed will be described wherein table hierarchy is reflected within the query design. The manner in which MSDsite applies pattern searching and short sequence alignment is performed using SQL queries will also be described where we use Oracle hint mechanism, 'LEADING' and 'USE_NL' to implicitly force access to nested tables by the primary key.

Barry Hardy
Douglas Connect
www.douglasconnect.com
Cheminformatics & Chemical Modelling in Drug Discovery: http://echeminfo.com/
Cheminfostream Blog: http://barryhardy.blogs.com/cheminfostream/

Compound optimization tools: Designed by scientists for scientists

Stephen Whiteley, LION Bioscience, presented the following seminar at the eCheminfo 2004 Web conference (http://echeminfo.com/), 8-19 November 2004:

Compound optimization tools: Designed by scientists for scientists

During the compound optimization stage of a discovery cycle, bench scientists are not only interested in figuring out the key structural features responsible for activity and selectivity, but also what structural changes need be made to improve these characteristics. In order to address these issues, a variety of search and analysis methodologies, along with visualization tools for structural and numerical data, are routinely deployed on the desktop. Unless these tools are easy-to-use, readily accessible and address relevant questions, bench scientists are seldom interested in using them. In collaboration with an external customer, LION has developed LeadNavigator, a spreadsheet tool that provides easy-to-use search, analysis and visualization capabilities. Interactive visualization and analysis capabilities include: chemistry-centric spreadsheet, 2D/3D scatter plots, profile and multi series plots, 2D/3D histograms, similarity searching, clustering, R-group deconvolution, molecular descriptors calculation, and others. LION will present an example of using these tools to address various questions raised during the lead compound optimization process

Barry Hardy
Douglas Connect
www.douglasconnect.com
Cheminformatics & Chemical Modelling in Drug Discovery: http://echeminfo.com/
Cheminfostream Blog: http://barryhardy.blogs.com/cheminfostream/

Non-parametric Methods for Identifying the Robust QSAR Models

Robert Clark, Senior Director of Research at Tripos, Inc., presented the following seminar at the eCheminfo 2004 Web conference (http://echeminfo.com), 8-19 November 2004:

Non-parametric Methods for Identifying the Robust QSAR Models

The vast majority of Quantitative Structure-Activity Relationship (QSAR) models employed in pharmaceutical and agrochemical discovery and development are based on some subset of the (very large) number of numerical descriptors available. Unfortunately, the datasets of interest share some problematic attributes. Firstly, the descriptors used are indirect to a greater or lesser degree, so that the “true” independent variables behind the model are latent – i.e., they are approximated by some linear (or nonlinear) combination of the descriptors at hand. Secondly, the descriptors used are usually intrinsically interdependent in a statistical sense, though that interdependence may be of rather complex form, especially if those descriptors have been pre-selected or constructed to not be linearly correlated.

Secondly, the underlying “universe” of chemical structures is inherently discrete, biased and non-redundant. The non-redundancy means that a structure will almost never appear twice in any “real world” data set. The bias reflects the fact that the likelihood of any particular structure appearing in a training set is dependent on the other structures in that training set – something that might be called the “chemical series problem.”

In classical parametric perturbation analysis, a small amount of IID (independent identically distributed) noise is added to each parameter in turn, and the response of the model performance to that noise is monitored. Unfortunately, the data structure characteristics cited above combine to make this approach more or less unsuitable for most QSAR models. Alternative nonparametric perturbation methods – i.e., methods that make no assumptions about the underlying intercorrelation structure of the data – will be described. These include applications in descriptor space (molecular holograms (HQSAR) and comparative molecular field analysis (CoMFA)) as well as the more broadly applicable response-space method known as progressive scrambling.

The recorded seminar and discussion is available through the eCheminfo site.

Barry Hardy
Douglas Connect
www.douglasconnect.com
Cheminformatics & Chemical Modelling in Drug Discovery: http://echeminfo.com/
Cheminfostream Blog: http://barryhardy.blogs.com/cheminfostream/

Pacific Rim Conference in Nanoscience brought from Australia to your PC

The InterNanotech Hub presents over the Internet the Program from the Pacific Rim Conference in Nanoscience, Broome, Australia, held 7-12 September 2004. The conference was chaired by Professor Nick Quirke, Imperial College London, and includes contributions from leading researchers in the Pacific Region, in addition to invited top speakers from Europe and the USA.

All lectures including slides and audio from the conference are available through the Web site located at http://internanotech.net/.

All virtual registrants may present a poster through the Web site for consideration for viewing and discussion at any time.

This conference is multidisciplinary, includes the disciplines of physics, chemistry, biology and engineering, and contains a significant amount of leading chemistry research in nanoscience.

The program covers current key research areas in nanoscience including nanofluidics, nanobio interfaces, nanofabrication, water, energy and minerals, nanoparticles, and computational nanoscience.

The virtual conference provides an opportunity to those who were unable to attend the physical conference in Broome, Australia to access and listen to all talks from the conference.

InterNanotech Conference abstracts and news and views are available on an ongoing basis through the Blog located at http://barryhardy.blogs.com/nanomosis/

Please visit http://internanotech.net/ for further program information and to signup.

Barry Hardy, Ph.D.
Douglas Connect, Switzerland
www.douglasconnect.com
+41 61 851 0170 (office)
Blogs:
On Nanotechnology - Nanomosis: http://barryhardy.blogs.com/nanomosis/
On Cheminformatics & Modelling - Cheminfostream: http://barryhardy.blogs.com/cheminfostream/

eCheminfo 2004 QSAR Session

David Lloyd, Trinity College Dublin, chaired the following QSAR session at the eCheminfo 2004 Web conference (http://echeminfo.com/), 8-19 November 2004:

Paul Blower, Leadscope, Correlating gene expression with classes of cytotoxic agents: identifying substrates and inhibitors of mdr1

Genomic studies are producing large databases of molecular information on cancers and other cell and tissue types. In order to utilize this data for drug discovery, we have developed a general analytical method for discovering relationships between compound classes and potential molecular targets. The procedure will be illustrated using the NCI-60 cell lines and microarray-based gene expression patterns. This provides a basis for associating groups of potentially novel compound classes with relevant gene families, followed by experimental validation. Our procedure involves the following steps: 1) Select an initial gene from the NCI60 gene expression database (in this case focused on genes involved in membrane transport); 2) use correlations between gene expression and drug potency to select a compound class; 3) Develop structure activity relationships within the compound class; 4) Query a genome-wide database to expand the molecular hypothesis on mechanism of cytotoxcity; 5) Validate the hypothesis experimentally; 6) Design new compounds for testing based on new molecular insights to avoid chemoresistance or enhance targeted chemosensitivity.

Michael B. Bolger, Simulations Plus, Inc., ADMET Predictions by Linking QSPR and Simulation

Oral bioavailability and biological activity of drugs can be broken down into seven components that are important considerations for drug development. The components are: Liberation from formulated product (dissolution, particle size, diffusivity, pKa, pH, food), Absorption from the lumen (permeability, lipophilicity, gastric emptying, intestinal transit, active transport, pH, pKa, protein binding), intestinal and hepatic first-pass Metabolism, Distribution into tissues, and subsequent Excretion, Response and Toxicity (LADMERT). The three computational tools that are employed for LADMERT predictions are: (1) statistical methods of quantitative molecular property relationships (QMPR) for biopharmaceutical property estimation, (2) computational chemistry methods for calculation of metabolism and enzyme-substrate interactions, and (3) mechanism-based physiological simulation methods for prediction of fraction absorbed and bioavailability. This seminar will focus on in silico estimation of log P, pKa, permeability, solubility, diffusivity, volume of distribution, blood-brain barrier penetration and protein binding. These properties have been shown to be the core properties required in computational biopharmaceutics. In addition, we will discuss the development of toxicity models based on a database of estrogen receptor ligands and on the maximum recommended therapeutic dose (MRTD). Finally, we will discuss a simulation of the gastrointestinal tract using the Advanced Compartmental Absorption and Transit (ACAT) model. This model has been applied to the prediction of fraction absorbed and the impact of physiological and biochemical factors on bioavailability. These in silico approaches have the potential to save valuable resources in the drug discovery, development, and regulatory processes.

Curt Breneman, Rensselaer Polytechnic Institute (NY), Graphical and automated analysis of the chemical information within hybrid shape/property descriptors in kernel-based models with feature selection

The field of predictive molecular property modeling spans a range of important subdisciplines within cheminformatics, and often results in the development of models that are difficult to interpret using traditional chemical intuition. Attempts to simplify or limit the number of descriptors used in a particular model result in tools that are more intuitively satisfying, but typically less capable of making validated predictions of molecular behavior to a useful level of accuracy. To address this deficiency, new tools for extracting chemical intuition from complex non-linear support vector machine (SVM) and kernel PLS (KPLS) models are to be presented. This toolkit includes graphical means for identifying key portions of molecules and pharmacophoric relationships that are represented within the model by combinations of otherwise non-intuitive descriptors. Through the development of these tools, the range of modeling techniques that can yield high-quality predictions as well as interpretable chemical knowledge will expand, and the applicability of these techniques will grow to incorporate a more diverse group of users – one that includes laboratory bench chemists.

One particular class of descriptors that has been found to contain useful chemical information are shape/property hybrids produced by the PEST program. When coupled with SVM or KPLS modeling methods, these alignment-free descriptors have been shown to outperform 3D QSAR methods such as CoMFA, without the need for cumbersome alignment rules. The presentation will illustrate some of these examples, and also demonstrate the use of graphical tools for extracting chemical meaning from descriptor patterns identified as important in the modeling process.

The recorded seminars and discussion are available through the eCheminfo site.

Barry Hardy
Douglas Connect
www.douglasconnect.com
Cheminformatics & Chemical Modelling in Drug Discovery: http://echeminfo.com/