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PubMed2002

Knowledge-based approaches in the design and selection of compound libraries for drug discovery.

In the past decade, the pharmaceutical industry has realized the increasing significance of impacting the early phase hit-to-lead development in the drug discovery process. In particular, knowledge-based approaches emerged and evolved to address a multitude of issues such as absorption, distribution, metabolism and excretion (ADME), potency, toxicity and overall drugability. Each of these approaches seeks to bring together all relevant pieces of information and create a knowledge-oriented process to deploy such information in drug discovery. This review focuses on work relating to drugability, which aims at obtaining hits (or leads) that have enhanced likelihoods of leading to successful clinical candidates by medicinal chemistry efforts. The period covered in this review is from 1997 (since the publication of Lipinski's rule of 5) to March 2002.

Animals↗

PubMedDate not supplied

Adding value to crystallographically-derived knowledge bases.

A protocol for the partially automated computational investigation of crystal structure geometries of transition-metal complexes with unusual/outlier structural features has been developed for application in an e-science context. This protocol not only is envisaged as a part of knowledge base software packages such as Mogul but can also be used to further analyze the results of database searches. The issues arising from automating the initial input generation and DFT optimization of complexes have been examined and a procedure for extracting additional knowledge "value" from the computational results is described. Potential problems/weaknesses arising from the choice of computational approach and from errors in the crystal structure refinement are discussed. A range of likely outcomes of applying this protocol to database mining results is illustrated, with representative examples identified for tetracoordinate transition-metal complexes and ligand fragments (terminal chloride, monodentate phosphorus(III), and primary amine ligands) with unusual metal-ligand bond lengths.

Computer Simulation↗

PubMed2001

Construction of a generic reaction knowledge base by reaction data mining.

As synthesis by combinatorial chemistry and high throughput screening have become well-established strategies in the drug discovery process, chemists face increased challenges in managing large amounts of data and using these data to design more diverse and focused libraries. As synthesis is an intuitive and empirical process, however, the classical approaches to computer-assisted synthesis planning do not fully satisfy the needs of the synthetic chemist. We describe a novel computational technique for extracting reaction data and building a generic reaction knowledge base (GRKB) to provide chemists with useful and well-organized knowledge. The method consists of three key steps: (1) the automatic recognition of reaction centers, (2) the definition of a hierarchy of reaction patterns, and (3) the organization of the generic reaction knowledge. Significant reaction knowledge has been discovered via mining a subset of the InfoChem Reaction database. A frame system has been constructed to store and retrieve the GRKB. Applications of this GRKB to synthesis planning are illustrated.

Artificial Intelligence↗

PubMed1997

Bioprocess supervision: neural networks and knowledge based systems.

Supervision of highly non-linear and time variant bioprocesses is of considerable importance to bioindustries as a means of achieving improved productivity and reduced process variability. Artificial intelligence methodologies, including artificial neural networks and knowledge based systems, can contribute significantly to the achievement of these objectives.

Biosensing Techniques↗

PubMed1992

A knowledge-based system for assisted ventilation of patients in intensive care units.

The procedure for weaning a patient with respiratory insufficiency from mechanical ventilation may be complex and requires expertise obtained by long clinical practice. We designed a knowledge-based system for the management of patients receiving respiratory support and implemented a weaning procedure. The system is intended for patients whose spontaneous respiratory activity is assisted by a Hamilton Veolar ventilator delivering a positive pressure plateau during inspiration (Pressure Support Ventilation mode). Our closed-loop real-time system running on a Personal Computer continuously adapts the assistance provided by the ventilator to the patient's evolution, and indicates when the patient can be withdrawn from the ventilator. Three parameters are used to appreciate the 'respiratory comfort' of the patient: breathing frequency, which we consider the most informative index, tidal volume and end-tidal CO2 pressure. A preliminary study of 19 patients was performed to evaluate the ability of our system to adapt the assistance to the patient's needs, with the main objective of facilitating weaning by gradually lowering the level of assistance. In 10 of these patients, considered as good candidates for weaning on the strength of objective criteria, the system maintained the breathing pattern in a zone of comfort for 95% of the period of assisted ventilation and stated that they were 'weanable'. This was consistent with the clinical evolution of all 10 patients. These results show that such a system can provide effective management for mechanically ventilated patients.

Adult↗

PubMedDate not supplied

Knowledge-based systems in medicine--a Nordic research and development programme.

A Nordic research and development programme, 'KBS in Medicine' (KUSIN-MEDICINE), was run in 1986-89. Its main goal was to acquire an understanding of applying knowledge-based techniques in medicine and of the limitations of present-day artificial intelligence (AI) methodologies. The programme comprised four experimental installation sites (Tampere in Finland, Uppsala and Linköping in Sweden, and Aalborg in Denmark) each prototyping in one or more medical domains. The programme was financed by the Nordic Fund for Technological and Industrial Development, by national funds for applied research and by a number of industries. Prototype decision support systems were produced in the following domains: intensive care (Tampere, Uppsala, Linköping, Aalborg), clinical chemistry (Tampere, Uppsala) and clinical neurophysiology (Aalborg in collaboration with Turku and Uppsala). These served to transfer this technology to industry and helped to identify limitations of this technology.

Artificial Intelligence↗

PubMed2001

Knowledge-based systems for automatic ventilatory management.

In intensive care and anesthesia, the demand for computerized medical assistants is potentially considerable, to filter and synthesize the growing mass of clinical parameters and information available. The authors detail how knowledge-based computerized assistants can be constructed for automatic ventilation management and report clinical results obtained with such a system for closed-loop control of pressure support ventilation and decision for extubation.

Algorithms↗

PubMed2004

A knowledge-based scale for the analysis and prediction of buried and exposed faces of transmembrane domain proteins.

MOTIVATION: The dearth of structural data on alpha-helical membrane proteins (MPs) has hampered thus far the development of reliable knowledge-based potentials that can be used for automatic prediction of transmembrane (TM) protein structure. While algorithms for identifying TM segments are available, modeling of the TM domains of alpha-helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains. RESULTS: A recent increase in the number of crystal structures of alpha-helical MPs has enabled an analysis of the lipid-exposed surfaces and the interiors of such molecules on the basis of structure, rather than sequence alone. Together with a conservation criterion that is based on previous observations that conserved residues are mostly found in the interior of MPs, the bias of certain residue types to be preferably buried or exposed is proposed as a criterion for predicting the lipid-exposed and interior faces of TMs. Applications to known structures demonstrates 80% accuracy of this prediction algorithm. AVAILABILITY: The algorithm used for the predictions is implemented in the ProperTM Web server (http://icb.med.cornell.edu/services/propertm/start).

Algorithms↗

PubMed2003

Structuring healthcare knowledge bases: an analysis of explicit and implicit structures in Arden Syntax and an XML schema representation of Arden Syntax.

The Arden Syntax for Medical Logic Modules (MLMs) is an ANSI and ISO recognized standard language for representing clinical knowledge bases. We analyzed the explicit and implicit structures in Arden Syntax MLMs and developed an information model represented as an XML schema. While the explicit structures were easily represented as XML, implicit structures require further explicit definition. In any future representation format, explicit structuring must be balanced with expressiveness and usability.

Artificial Intelligence↗

PubMed2001

Domestic violence: level of training, knowledge base and practice among Milwaukee physicians.

BACKGROUND: Domestic violence is a prevalent problem with significant health consequences. Early recognition and appropriate intervention with referral to local domestic violence agencies can be life-saving. Little is known, however, about the current level of training, knowledge base and attitudes of physicians in this area. METHODS: A survey was sent to 1300 physicians practicing in Milwaukee County in the following specialties: Family Practice, Internal Medicine, OB/GYN, Psychiatry. Demographic information was obtained. Questions were designed to explore attitudes towards domestic violence, frequency of encounters with victims or abusers, and knowledge of resources and appropriate intervention. RESULTS: Of the 192 respondents, 74% reported having some training in domestic violence. Thirty percent reported seeing victims in their practice on a daily or weekly basis. Seventy percent feel able to identify a victim of domestic violence. Less than a third of respondents screened at least half of the patients they see for the possibility of abuse. Less than half always refer victims to a hotline or shelter, and less than a quarter of the respondents discuss safety plans with victims. A potentially dangerous response is telling a victim not to go back to an abuser without providing referrals and safety supports. In spite of this, almost a quarter of respondents always tell a victim to not go back to the abuser. Family practitioners and psychiatrists were more likely to discuss abuse with patients than were internists. CONCLUSION: Significant numbers of physicians, in Milwaukee County, practicing certain specialties that potentially have a high rate of contact with domestic violence victims have had insufficient training in domestic violence assessment and intervention. Physicians should be familiar with the domestic violence hotlines and shelters in their communities and need to incorporate screen questions for domestic violence into their regular practice.

Attitude of Health Personnel↗

PubMed1996

A knowledge-based method for protein structure refinement and prediction.

The native conformation of a protein, in a given environment, is determined entirely by the various interatomic interactions dictated by the amino acid sequence (1-3). We describe here a knowledge-based approach for protein structure assessment and prediction. Using a well-defined set of high-resolution protein structures, we have derived statistical potentials, in the form of atom-pairwise distance probability density functions. These provide a description of pairwise interatomic interactions of native proteins. When applied to highly randomized and noisy structures of proteins distinct from the basis set, native-like structures were obtained to very high precision (< or = 2A). The examples tested include proteins of all sizes (from 38 up to 461 amino acids long) and diverse topological structures (alpha, beta and alpha-beta classes). The potentials appear to be sensitive enough to recognize subtle distortions from a native packing structure and in optimization of structures drive them consistently to a higher probability. Therefore they provide a powerful tool for refinement of X-ray and NMR derived structures at arbitrary degrees of initial precision.

Crystallography, X-Ray↗

PubMed1999

Modeling helix-turn-helix protein-induced DNA bending with knowledge-based distance restraints.

A crucial element of many gene functions is protein-induced DNA bending. Computer-generated models of such bending have generally been derived by using a presumed bending angle for DNA. Here we describe a knowledge-based docking strategy for modeling the structure of bent DNA recognized by a major groove-inserting alpha-helix of proteins with a helix-turn-helix (HTH) motif. The method encompasses a series of molecular mechanics and dynamics simulations and incorporates two experimentally derived distance restraints: one between the recognition helix and DNA, the other between respective sites of protein and DNA involved in chemical modification-enabled nuclease scissions. During simulation, a DNA initially placed at a distance was "steered" by these restraints to dock with the binding protein and bends. Three prototype systems of dimerized HTH DNA binding were examined: the catabolite gene activator protein (CAP), the phage 434 repressor (Rep), and the factor for inversion stimulation (Fis). For CAP-DNA and Rep-DNA, the root mean square differences between model and x-ray structures in nonhydrogen atoms of the DNA core domain were 2.5 A and 1.6 A, respectively. An experimental structure of Fis-DNA is not yet available, but the predicted asymmetrical bending and the bending angle agree with results from a recent biochemical analysis.

Base Sequence↗

PubMedDate not supplied

Molecular diversity in chemical databases: comparison of medicinal chemistry knowledge bases and databases of commercially available compounds.

A molecular descriptor space has been developed which describes structural diversity. Large databases of molecules have been mapped into it and compared. This analysis used five chemical databases, CMC and MDDR, which represent knowledge bases containing active medicinal agents, ACD and SPECS, two databases of commercially available compounds, and finally the Wellcome Registry. Together these databases contained more than 300,000 structures. Topological indices and the free energy of solvation were computed for each compound in the databases. Factor analysis was used to reduce the dimensionality of the descriptor space. Low density observations were deleted as a way of removing outliers, which allowed a further reduction in the descriptor space of interest. The five databases could then be compared on an efficient basis using a metric developed for this purpose. A Riemann gridding scheme was used to subdivide the factor space into subhypercubes to obtain accurate comparisons. Most of the 300,000 structures were highly clustered, but unique structures were found. An analysis of overlap between the biological and commercial databases was carried out. The metric provides a useful algorithm for choosing screening sets of diverse compounds from large databases.

Artificial Intelligence↗

PubMed2004

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality.

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Journal Article↗

PubMed2003

Contributions of qualitative research to the knowledge base of normal communication.

As clinical speech-language pathology moves toward a progressive use of qualitative research methodologies and applications for clinical purposes, it is helpful to know how the qualitative paradigm has influenced our field. This article reviews a number of studies from the social sciences and their impact on our knowledge of the properties of communication and the investigation of cognitive and language acquisition. This review indicates that qualitative research currently has a significant impact on our knowledge bases in clinical speech-language pathology.

Communication↗

PubMed1989

Knowledge-based approach to intelligent alarms.

The goal of intelligent alarms is not only to recognize potentially dangerous situations, but to discriminate whether the condition is truly threatening or has resulted from nonthreatening causes, such as artifacts. The authors describe a knowledge-based approach in the development of intelligent alarms, using complex guidelines that simulate human reasoning and follow "if, then" rules of problem solving.

Anesthesia, General↗

PubMed2003

Chemogenomics knowledge-based strategies in drug discovery.

In the postgenomic age of drug discovery, targets can no longer be viewed as singular objects having no relationship to one another. All targets are now visible and the systematic exploration of selected target families appears to be a promising way to speed up and further industrialize target-based drug discovery. Chemogenomics refers to such systematic exploration of target families and aims to identify all possible ligands of all target families. Because biology works by applying prior knowledge to an unknown entity, chemogenomics approaches are expected to be especially effective within the previously well-explored target families, for which, in addition to the protein sequence and structure information, considerable knowledge of pharmacologically active structural classes and structure-activity relationships exists. For the new target families, chemical knowledge will have to be generated and beyond biological target validation, the emphasis is on chemistry to provide the molecules with which their novel biology and pharmacology can be studied. Using examples from the previously most successfully explored target families, the GPCR family in particular, we summarize herein our current chemogenomics knowledge-based strategies for drug discovery, which are founded on the high integration of chem and bioinformatics, thereby providing a molecular informatics frame for the exploration of the new target families.

Computational Biology↗

PubMed2001

Pharmacogenetics research network and knowledge base: 1st annual scientific meeting.

The National Institute of General Medical Sciences of the National Institutes of Health recently established a collaborative group of scientists, called the Pharmacogenetics Research Network. Central to the network is a shared, state-of-the-art data repository, the Pharmacogenetics Knowledge Base (PharmGKB), which is housed at Stanford University. Network investigators deposit pharmacogenetic data into PharmGKB, after all individually identifying information has been removed. Contents of PharmGKB will be freely accessible to the scientific community, with the goal of forging new links between gene variation and drug response. An open scientific meeting was held recently to introduce the research community to the network and to invite academic and industry-based researchers to deposit data into PharmGKB. Featured at the meeting were summaries of research progress to date, as well as discussions of issues intimately related to pharmacogenetics research, namely ethics and relations with the biotechnology and pharmaceutical industries.

Animals↗