Multiobjective optimization of combinatorial libraries.

Combinatorial chemistry and high-throughput screening have caused a fundamental shift in the way chemists contemplate experiments. Designing a combinatorial library is a controversial art that involves a heterogeneous mix of chemistry, mathematics, economics, experience, and intuition. Although there seems to be little agreement as to what constitutes an ideal library, one thing is certain: only one property or measure seldom defines the quality of the design. In most real-world applications, a good experiment requires the simultaneous optimization of several, often conflicting, design objectives, some of which may be vague and uncertain. In this paper, we discuss a class of algorithms for subset selection rooted in the principles of multiobjective optimization. Our approach is to employ an objective function that encodes all of the desired selection criteria, and then use a simulated annealing or evolutionary approach to identify the optimal (or a nearly optimal) subset from among the vast number of possibilities. Many design criteria can be accommodated, including diversity, similarity to known actives, predicted activity and/or selectivity determined by quantitative structure-activity relationship (QSAR) models or receptor binding models, enforcement of certain property distributions, reagent cost and availability, and many others. The method is robust, convergent, and extensible, offers the user full control over the relative significance of the various objectives in the final design, and permits the simultaneous selection of compounds from multiple libraries in full- or sparse-array format.

Ageing research in Greece.

Ageing research in Greece is well established. Research groups located in universities, research institutes or public hospitals are studying various and complementary aspects of ageing. These research activities include (a) functional analysis of Clusterin/Apolipoprotein J, studies in healthy centenarians and work on protein degradation and the role of proteasome during senescence at the National Hellenic Research Foundation; (b) regulation of cell proliferation and tissue formation, a nationwide study of determinants and markers of successful ageing in Greek centenarians and studies of histone gene expression and acetylation at the National Center for Scientific Research, Demokritos; (c) work on amyloid precursor protein and Presenilin 1 at the University of Athens; (d) oxidative stress-induced DNA damage and the role of oncogenes in senescence at the University of Ioannina; (e) studies in the connective tissue at the University of Patras; (f) proteomic studies at the Biomedical Sciences Research Center Alexander Fleming; (g) work on Caenorhabditis elegans at the Foundation for Research and Technology; (h) the role of ultraviolet radiation in skin ageing at Andreas Sygros Hospital; (i) follow-up studies in healthy elderly at the Athens Home for the Aged; and (j) socio-cultural aspects of ageing at the National School of Public Health. These research activities are well recognized by the international scientific community as it is evident by the group's very good publication records as well as by their direct funding from both European Union and USA. This article summarizes these research activities and discuss future directions and efforts towards the further development of the ageing field in Greece.

Variable selection for QSAR by artificial ant colony systems.

Derivation of quantitative structure-activity relationships (QSAR) usually involves computational models that relate a set of input variables describing the structural properties of the molecules for which the activity has been measured to the output variable representing activity. Many of the input variables may be correlated, and it is therefore often desirable to select an optimal subset of the input variables that results in the most predictive model. In this paper we describe an optimization technique for variable selection based on artificial ant colony systems. The algorithm is inspired by the behavior of real ants, which are able to find the shortest path between a food source and their nest using deposits of pheromone as a communication agent. The underlying basic self-organizing principle is exploited for the construction of parsimonious QSAR models based on neural networks for several classical QSAR data sets.

Multiobjective optimization of combinatorial libraries.

Combinatorial chemistry and high-throughput screening have caused a fundamental shift in the way chemists contemplate experiments. Designing a combinatorial library is a controversial art that involves a heterogeneous mix of chemistry, mathematics, economics, experience, and intuition. Although there seems to be little agreement as to what constitutes an ideal library, one thing is certain: only one property or measure seldom defines the quality of the design. In most real-world applications, a good experiment requires the simultaneous optimization of several, often conflicting, design objectives, some of which may be vague and uncertain. In this paper, we discuss a class of algorithms for subset selection rooted in the principles of multiobjective optimization. Our approach is to employ an objective function that encodes all of the desired selection criteria, and then use a simulated annealing or evolutionary approach to identify the optimal (or a nearly optimal) subset from among the vast number of possibilities. Many design criteria can be accommodated, including diversity, similarity to known actives, predicted activity and/or selectivity determined by quantitative structure-activity relationship (QSAR) models or receptor binding models, enforcement of certain property distributions, reagent cost and availability, and many others. The method is robust, convergent, and extensible, offers the user full control over the relative significance of the various objectives in the final design, and permits the simultaneous selection of compounds from multiple libraries in full- or sparse-array format.

Combinatorial networks.

A novel approach for the analysis and virtual screening of large combinatorial libraries is presented. The method attempts to relieve the computational burden by computing the properties of the products in a way that does not require their explicit enumeration. In particular, a small subset of compounds from the virtual library is identified and their descriptors are calculated in a conventional manner. The resulting data is used as input to a multilayer perceptron, which is trained to predict the descriptors of the products from the descriptors of their respective building blocks. Once trained, the neural network is able to estimate the descriptors of the remaining members of the virtual library with remarkable accuracy, without ever, generating their connection tables. This method eliminates the two most time-consuming steps in virtual screening and allows the processing of very large combinatorial libraries that are intractable with conventional techniques.

Design and prioritization of plates for high-throughput screening.

A general algorithm for the prioritization and selection of plates for high-throughput screening is presented. The method uses a simulated annealing algorithm to search through the space of plate combinations for the one that maximizes some user-defined objective function. The algorithm is robust and convergent, and permits the simultaneous optimization of multiple design objectives, including molecular diversity, similarity to known actives, predicted activity or binding affinity, and many others. It is shown that the arrangement of compounds among the plates may have important consequences on the ability to design a well-targeted and cost-effective experiment. To that end, two simple and effective schemes for the construction of homogeneous and heterogeneous plates are outlined, using a novel similarity sorting algorithm based on one-dimensional nonlinear mapping.

A novel method for building regression tree models for QSAR based on artificial ant colony systems.

Among the multitude of learning algorithms that can be employed for deriving quantitative structure-activity relationships, regression trees have the advantage of being able to handle large data sets, dynamically perform the key feature selection, and yield readily interpretable models. A conventional method of building a regression tree model is recursive partitioning, a fast greedy algorithm that works well in many, but not all, cases. This work introduces a novel method of data partitioning based on artificial ants. This method is shown to perform better than recursive partitioning on three well-studied data sets.

A constant time algorithm for estimating the diversity of large chemical libraries.

We describe a novel diversity metric for use in the design of combinatorial chemistry and high-throughput screening experiments. The method estimates the cumulative probability distribution of intermolecular dissimilarities in the collection of interest and then measures the deviation of that distribution from the respective distribution of a uniform sample using the Kolmogorov-Smirnov statistic. The distinct advantage of this approach is that the cumulative distribution can be easily estimated using probability sampling and does not require exhaustive enumeration of all pairwise distances in the data set. The function is intuitive, very fast to compute, does not depend on the size of the collection, and can be used to perform diversity estimates on both global and local scale. More importantly, it allows meaningful comparison of data sets of different cardinality and is not affected by the curse of dimensionality, which plagues many other diversity indices. The advantages of this approach are demonstrated using examples from the combinatorial chemistry literature.

HIV risk behaviour correlates among injecting drug users in Greek prisons.

AIMS: To identify the correlates of injecting drug use within prison. DESIGN: A national cross-sectional study, participation being voluntary and anonymous. SETTING: Ten Greek prisons. PARTICIPANTS: A representative sample of 1000 male inmates; 861 questionnaires were completed and analysed. MEASUREMENT: A self-report questionnaire for demographics, penal history, drug use and sharing injecting equipment. FINDINGS: Two hundred and ninety inmates (33.7%) reported injecting drugs at some time in their lives, of whom 174 (60%) had injected while imprisoned. Among those who had injected while imprisoned, 145 (83%) had shared equipment while incarcerated. Logistic regression analysis suggested that total time in prison, previous drug conviction, being a convict (as opposed to on remand) and having multiple female sexual partners 1 year before incarceration were significant HIV risk behaviour correlates. For every year of imprisonment, the risk of injection in prison increased by about 17% [OR = 1.17 (95% CI: 1.07-1.27)]. Inmates with a previous drug-related conviction were about twice as likely to inject within prison [OR = 1.97 (95% CI: 1.16-3.33)]. Finally, convicted inmates were marginally significantly more prone to inject in prison [OR = 1.58 (95% CI: 0.92-2.74)]. CONCLUSIONS: Variables related to the inmates' prison career influence HIV risk behaviours within prison. There is a need to assist IDUs in reducing the likelihood of high-risk behaviour by considering factors such as frequency of incarceration, length of time incarcerated and availability of detoxification programmes within prison.

Nonlinear mapping networks.

Among the many dimensionality reduction techniques that have appeared in the statistical literature, multidimensional scaling and nonlinear mapping are unique for their conceptual simplicity and ability to reproduce the topology and structure of the data space in a faithful and unbiased manner. However, a major shortcoming of these methods is their quadratic dependence on the number of objects scaled, which imposes severe limitations on the size of data sets that can be effectively manipulated. Here we describe a novel approach that combines conventional nonlinear mapping techniques with feed-forward neural networks, and allows the processing of data sets orders of magnitude larger than those accessible with conventional methodologies. Rooted on the principle of probability sampling, the method employs a classical algorithm to project a small random sample, and then "learns" the underlying nonlinear transform using a multilayer neural network trained with the back-propagation algorithm. Once trained, the neural network can be used in a feed-forward manner to project the remaining members of the population as well as new, unseen samples with minimal distortion. Using examples from the fields of image processing and combinatorial chemistry, we demonstrate that this method can generate projections that are virtually indistinguishable from those derived by conventional approaches. The ability to encode the nonlinear transform in the form of a neural network makes nonlinear mapping applicable to a wide variety of data mining applications involving very large data sets that are otherwise computationally intractable.

Kolmogorov-Smirnov statistic and its application in library design.

After several years of frantic development, the dream of an "ideal" library remains elusive. Traditionally, combinatorial chemistry has been used primarily for lead generation, and molecular diversity has been the method of choice for designing and prioritizing experiments. One aspect that often has been overlooked is the drug likeness of the resulting collections. Recently, there have been several attempts to quantify this concept and incorporate it directly into the design process. This article demonstrates the limitations of some conventional methodologies and proposes a new paradigm for experimental design based on the principles of multiobjective optimization. This method allows traditional design objectives such as diversity or similarity to be combined with secondary selection criteria in order to bias the selection toward more pharmacologically relevant regions of chemical space. The method is robust, general, and easily extensible, and it allows the medicinal chemist to create designs that represent the best compromise between several, often conflicting, objectives. Two types of designs are discussed (singles, arrays), and a novel criterion based on the Kolmogorov-Smirnov statistic is proposed as a means to enforce a particular distribution on key molecular properties that are related to drug likeness. The potential of this approach is illustrated in the design of an exploratory library based on the simultaneous optimization of five different parameters. These parameters are combined in an intuitive manner to produce a design that is sufficiently diverse, exhibits a molecular weight and logP profile that is consistent with the respective distributions of known drugs, requires a small number of reagents, and can be synthesized easily in array format using robotic hardware.

Advances in diversity profiling and combinatorial series design.

Rapid advances in synthetic and screening technology have recently enabled the simultaneous synthesis and biological evaluation of large chemical libraries containing hundreds to tens of thousands of compounds, using molecular diversity as a means to design and prioritize experiments. This paper reviews some of the most important computational work in the field of diversity profiling and combinatorial library design, with particular emphasis on methodology and applications. It is divided into four sections that address issues related to molecular representation, dimensionality reduction, compound selection, and visualization.

A new method for analyzing protein sequence relationships based on Sammon maps.

Recent advances in gene sequencing and rational drug design have re-emphasized the need for new methods for protein analysis, classification, and structure and function prediction. In this article, we introduce a new method for analyzing protein sequences based on Sammon's non-linear mapping algorithm. When applied to a family of homologous sequences, the method is able to capture the essential features of the similarity matrix, and provides a faithful representation of chemical or evolutionary distance in a simple and intuitive way. The merits of the new algorithm are demonstrated using examples from the protein kinase family.

PD 90780, a non peptide inhibitor of nerve growth factor's binding to the P75 NGF receptor.

Nerve growth factor is a peptide that supports the survival and differentiation of discrete neuronal populations in the peripheral and central nervous systems. NGF binds to both trkA, a tyrosine kinase receptor, and to the p75 nerve growth factor receptor, a protein lacking a consensus signalling sequence. We have identified a substituted pyrazoloquinazolinone, PD 90780, which inhibits binding of nerve growth factor to the p75 receptor. This inhibition of binding occurs due to PD 90780 binding to nerve growth factor, not to the p75 receptor. This compound may be useful in identifying the region(s) of nerve growth factor involved in binding to the p75 receptor and in clarifying the role of p75 receptor in the actions of the neurotrophins.

AIDS in Greece.

PIP: While Greece ranks among European countries with the lowest rate of AIDS, at least 689 AIDS cases and an estimated 10,000-15,000 people are nonetheless infected with HIV in the country. Despite the specter of a growing AIDS epidemic in Greece, the AIDS information campaign began in the mid 1980s no longer exists and the National Committee for AIDS has been replaced by the National Agency for Infectious Diseases. Health care professionals voice concern over the need to control blood products imported from Europe, while broad debate exists over whether or not to create special hospital units in which to treat people with AIDS. Further, HIV and AIDS have sparked the discussion and study of sexuality and its sociocultural dimensions; the Ministry of Education has prepared a program for prevention and health promotion to be used in schools; and AIDS/HIV support groups are gradually emerging.^ieng