PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10680113

VisualiSAR: a web-based application for clustering, structure browsing, and structure-activity relationship study.

D J Wild·C J Blankley
View original source ↗Export metadata ↓

Abstract

VisualiSAR is a program designed to display chemical structures, find similarities and differences between them, and highlight relationships that might exist. The program integrates cluster analysis for the grouping of structurally related compounds, modal analysis of molecular fingerprints for the sorting and highlighting of chemical features, and a Web-based interface for flexibility and ease of use. VisualiSAR has proved useful for a number of applications including the discernment of structure-activity relationships (SAR) of high-volume screening data, and general structure browsing. This article discusses the design of the tool and illustrates two applications.

Explore related subjects

Computer SimulationInternetModels, MolecularMolecular ConformationOrganic ChemicalsSoftwareStructure-Activity RelationshipJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D J Wild, C J Blankley. 1999. VisualiSAR: a web-based application for clustering, structure browsing, and structure-activity relationship study.. https://doi.org/10.1016/s1093-3263(99)00026-1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗
PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗
PubMed2001

Integration of information systems: assessing its quality.

Due to organizational and technological changes the need for integrating information systems within healthcare institutions, has increased enormously. Although the technical means for systems integration have definitely matured, integration methodologies are still in their infancy. Two important questions regarding systems integration are hardly ever addressed in a systematic way: how to derive integration requirements, and how to check whether the requirements are met in a given integrated system. These two questions must be answered if we want to assess or improve the quality of integration of a given set of systems. In this article we present a nine-step method for deriving integration requirements from a business process model, and we assess the quality of integration of a given integrated system against these requirements. The method is demonstrated by elaborating two case studies from the health care domain.

Computer Simulation↗