Biomedical subjects

Kamal Azzaoui

Publications and source records attributed to Kamal Azzaoui.

6 recordsLinked to original sources

PubMed2005

A chemoinformatics analysis of hit lists obtained from high-throughput affinity-selection screening.

The high-throughput affinity-selection screening platform SpeedScreen was recently reported by the Novartis Institutes for BioMedical Research as a homogeneous, label-free screening technology with mass-spectrometry readout. SpeedScreen relies on the screening of compound mixtures with various target proteins and uses fast size-exclusion chromatography to separate target-bound from unbound substances. After disintegration of the target-binder complex, the binder molecules are identified by their molecular masses using liquid chromatography/mass spectrometry. The authors report an analysis of the molecular properties of hits obtained with SpeedScreen on 26 targets screened within the past few years at Novartis using this technology. Affinity-based SpeedScreen is a robust high-throughput screening technology that does not accumulate frequent hitters or potential covalent binders. The hits are representative of the most commonly identified scaffold classes observed for known drugs. Validated SpeedScreen hits tend to be enriched on more lipophilic and larger-molecular-weight compounds compared to the whole library. The potential for a reduced SpeedScreen screening set to be used in case only limited protein quantities are available is evaluated. Such a reduced compound set should also maximize the coverage of the high-performing regions of the chemical property and class spaces; chemoinformatics methods including genetic algorithms and divisive K-means clustering are used for this aim.

Chromatography, High Pressure Liquid↗

PubMed2005

Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information.

We test the hypothesis that fusing the outputs of similarity searches based on a single bioactive reference structure and on its nearest neighbors (of unknown activity) is more effective (in terms of numbers of high-ranked active structures) than a similarity search involving just the reference structure. This turbo similarity searching approach provides a simple way to enhance the effectiveness of simulated virtual screening searches of the MDL Drug Data Report database.

Computing Methodologies↗

PubMed2005

Key aspects of the Novartis compound collection enhancement project for the compilation of a comprehensive chemogenomics drug discovery screening collection.

The NIBR (Novartis Institutes for BioMedical Research) compound collection enrichment and enhancement project integrates corporate internal combinatorial compound synthesis and external compound acquisition activities in order to build up a comprehensive screening collection for a modern drug discovery organization. The main purpose of the screening collection is to supply the Novartis drug discovery pipeline with hit-to-lead compounds for today's and the future's portfolio of drug discovery programs, and to provide tool compounds for the chemogenomics investigation of novel biological pathways and circuits. As such, it integrates designed focused and diversity-based compound sets from the synthetic and natural paradigms able to cope with druggable and currently deemed undruggable targets and molecular interaction modes. Herein, we will summarize together with new trends published in the literature, scientific challenges faced and key approaches taken at NIBR to match the chemical and biological spaces.

Animals↗

PubMed2004

Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures.

This paper reports a detailed comparison of a range of different types of 2D fingerprints when used for similarity-based virtual screening with multiple reference structures. Experiments with the MDL Drug Data Report database demonstrate the effectiveness of fingerprints that encode circular substructure descriptors generated using the Morgan algorithm. These fingerprints are notably more effective than fingerprints based on a fragment dictionary, on hashing and on topological pharmacophores. The combination of these fingerprints with data fusion based on similarity scores provides both an effective and an efficient approach to virtual screening in lead-discovery programmes.

Algorithms↗

PubMedDate not supplied

Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures.

Fingerprint-based similarity searching is widely used for virtual screening when only a single bioactive reference structure is available. This paper reviews three distinct ways of carrying out such searches when multiple bioactive reference structures are available: merging the individual fingerprints into a single combined fingerprint; applying data fusion to the similarity rankings resulting from individual similarity searches; and approximations to substructural analysis. Extended searches on the MDL Drug Data Report database suggest that fusing similarity scores is the most effective general approach, with the best individual results coming from the binary kernel discrimination technique.

Molecular Structure↗

PubMedDate not supplied

New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching.

Similarity searching using a single bioactive reference structure is a well-established technique for accessing chemical structure databases. This paper describes two extensions of the basic approach. First, we discuss the use of group fusion to combine the results of similarity searches when multiple reference structures are available. We demonstrate that this technique is notably more effective than conventional similarity searching in scaffold-hopping searches for structurally diverse sets of active molecules; conversely, the technique will do little to improve the search performance if the actives are structurally homogeneous. Second, we make the assumption that the nearest neighbors resulting from a similarity search, using a single bioactive reference structure, are also active and use this assumption to implement approximate forms of group fusion, substructural analysis, and binary kernel discrimination. This approach, called turbo similarity searching, is notably more effective than conventional similarity searching.

Artificial Intelligence↗