Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation

Geppert, Hanna; Vogt, Martin; Bajorath, Jürgen

doi:10.1021/ci900419k

Cited by 313 publications

(277 citation statements)

References 113 publications

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“…Among the various approaches reported sofar to represent chemical space [14][15][16], MQN-space has the advantage of simplicity. For instance chemical spaces have been constructed by assigning dimensions to descriptors such as eigenvalues of matrices constructed from primary descriptors [13,17,18], or more directly to descriptors of molecular structure and calculated physicochemical properties [19,20], or by using various types of fingerprints [21].…”

Section: Introductionmentioning

confidence: 99%

Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem

Deursen

Blum

Reymond

2011

J Comput Aided Mol Des

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The 4.5 million organic molecules with up to 20 non-hydrogen atoms in PubChem were analyzed using the MQN-system, which consists in 42 integer value descriptors of molecular structure. The 42-dimensional MQN-space was visualised by principal component analysis and representation of the (PC1, PC2), (PC1, PC3) and (PC2, PC3) planes. The molecules were organized according to ring count (PC1, 38% of variance), the molecular size (PC2, 25% of variance), and the H-bond acceptor count (PC3, 12% of variance). Compounds following Lipinski's bioavailability, Oprea's lead-likeness and Congreve's fragment-likeness criteria formed separated groups in MQN-space visible in the (PC2, PC3) plane. MQN-similarity searches of the 4.5 million molecules (see the browser available at www.gdb.unibe.ch) gave significant enrichment factors for recovering groups of fragment-sized bioactive compounds related to ten different biological targets taken from Chembl, allowing leadhopping relationships not seen with substructure fingerprint similarity searches. The diversity of different compound series was analyzed by MQN-distance histograms.

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Section: Introductionmentioning

confidence: 99%

Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem

Deursen

Blum

Reymond

2011

J Comput Aided Mol Des

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“…The approach was rapidly and widely adopted and is now a standard feature in chemoinformatics software systems. The precise way in which it is implemented does, of course, vary from system to system, and there is still much discussion as to how to maximise the effectiveness of searching [7,9,48]. Two important areas of current research are data fusion and scaffold hopping, as discussed further below.…”

Section: Similarity Searchingmentioning

confidence: 99%

“…In group fusion, alternatively, multiple reference structures are searched against a database using a single similarity measure. The latter approach obviously requires the availability of multiple bioactive molecules, rather than just a single one as in conventional similarity searching, but has been found to be notably more effective in operation [6,48,51]. Current areas of research include, e.g., how different rankings should be combined [52] and whether it is possible to provide a theoretical model of the fusion process [53].…”

Section: Similarity Searchingmentioning

confidence: 99%

Similarity‐based data mining in files of two‐dimensional chemical structures using fingerprint measures of molecular resemblance

Willett

2011

WIREs Data Min & Knowl

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This paper reviews the use of measures of inter-molecular similarity for processing databases of chemical structures, which play an important role in the discovery of new drugs by the pharmaceutical industry. The similarity measures considered here are based on the use of a fingerprint representation of molecular structure, where a fingerprint is a vector encoding the presence of fragment substructures in a molecule and where the similarity between pairs of such fingerprints is computed using an association coefficient such as the Tanimoto coefficient. The Similar Property Principle provides the basic rationale for the use of similarity methods in three important chemoinformatics applications: similarity searching, database clustering, and molecular diversity analysis. Similarity searching enables the identification of those molecules in a database that are most similar to a userdefined, biologically active query molecule, with data fusion providing an effective way of combining the results of multiple similarity searches. Cluster analysis, typically using the Jarvis-Patrick, Ward or divisive k-means clustering methods, enables the cost-effective selection of molecules for biological testing, for property prediction and for investigating database overlap. Molecular diversity analysis, typically using cluster-based, dissimilarity-based or optimisation-based approaches, enables the identification of structurally diverse sets of molecules, so as to ensure that the full chemical space spanned by a database is tested in the search for novel bioactive molecules.

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“…Os métodos de LBVS e SBVS são alternativas eficientes e 43 Para a geração de modelos de QSAR 2D e 3D, é indispensável a organização de conjuntos padrões de dados. Os compostos devem pertencer à mesma classe química ou possuir um farmacóforo comum.…”

Section: Triagem Virtual Baseada Na Estrutura Do Liganteunclassified

“…40,45 Etapas de LBVS Em geral, os métodos de LBVS se fundamentam em estudos de similaridade ou em modelos construídos a partir de conjuntos de ligantes e não ligantes. 40,43 As buscas por similaridade química envolvem a geração de impressões digitais moleculares para todas as moléculas das bases de dados. Esta etapa evolui para a comparação de cada molécula das bases com um ligante de referência (i.e., um composto com atividade biológica conhecida), o que permite a classificação dos compostos em relação a sua similaridade química (Figura 3a).…”

Section: Triagem Virtual Baseada Na Estrutura Do Liganteunclassified

Integração das técnicas de triagem virtual e triagem biológica automatizada em alta escala: oportunidades e desafios em P&D de fármacos

Ferreira¹,

Oliva²,

Andricopulo³

2011

Quím. Nova

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Recebido em 16/1/11; aceito em 2/6/11; publicado na web em 26/7/11 INTEGRATING VIRTUAL AND HIGH-THROUGHPUT SCREENING: OPPORTUNITIES AND CHALLENGES IN DRUG RESEARCH AND DEVELOPMENT. High-throughput screening (HTS) and virtual screening (VS) are useful methods employed in drug discovery, allowing the identification of promising hits for lead optimization. The efficiency of these approaches depends on a number of factors, such as the organization of high quality databases of compounds and the parameterization of essential components of the screen process. This brief review presents the basic principles of the HTS and VS methods, as well as a perspective of the utility and integration of these drug design approaches, highlighting current opportunities and future challenges in medicinal chemistry.Keywords: high-throughput screening; virtual screening; drug design. INTRODUÇÃOA descoberta de novos fármacos é um grande desafio para a indústria farmacêutica moderna. Os altos investimentos na área de pesquisa e desenvolvimento (P&D) contrastam com o número de novos medicamentos que têm chegado ao mercado nos últimos anos. 1 Este complexo panorama tem forçado a adoção de novas estratégias com o objetivo de aumentar a eficiência do processo de P&D, tendo como alicerces as inovações científicas, tecnológicas e empresariais.Considerando-se o expressivo número de alvos biológicos (proteínas alvo) promissores para o planejamento de fármacos, as técnicas de triagem biológica automatizada em alta escala (HTS -high throughput screening) e de triagem virtual (VS -virtual screening) têm ocupado papel de destaque entre as estratégias modernas exploradas na identificação de novas substâncias bioativas. Desde o seu surgimento, a HTS tem sido amplamente utilizada pela indústria farmacêutica, propiciando a avaliação de milhões de compostos contra proteínas alvo. Embora esta estratégia tenha despertado a esperança de uma revolução na descoberta de substâncias bioativas, em pouco tempo as suas limitações ficaram evidentes. 2 A ocorrência de um número elevado de falso-positivos, identificados inicialmente como hits (termo em inglês para designar um novo ligante ou composto bioativo) nos ensaios bioquímicos em alta escala pode ser destacada como um dos grandes problemas. 3 Os múltiplos modos de interação ou mecanismos de ação apresentados pelos diferentes hits selecionados podem também ser mencionados como outros importantes obstácu-los. 4 Além disso, o número possível de compostos com propriedades fármaco-similar (drug-like) 5 é várias ordens de magnitude maior do que o número de compostos que pode ser analisado nos processos de HTS. Portanto, o planejamento das bases de dados de compostos é essencial, sendo que fatores como propriedades moleculares e físico-químicas, ou ainda a diversidade química, devem ser considerados. [6][7][8] Com os notáveis avanços da biologia estrutural e a disponibilidade de recursos computacionais de alto desempenho, a VS surgiu como importante alternativa à HTS. A VS é um método in silico ("computacional") empre...

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Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation

Cited by 313 publications

References 113 publications

Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem

Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem

Similarity‐based data mining in files of two‐dimensional chemical structures using fingerprint measures of molecular resemblance

Integração das técnicas de triagem virtual e triagem biológica automatizada em alta escala: oportunidades e desafios em P&D de fármacos

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