The ‘SAR Matrix’ method and its extensions for applications in medicinal chemistry and chemogenomics

Assessing the degree to which analogue series are chemically saturated is of major relevance in compound optimization. Decisions to continue or discontinue series are typically made on the basis of subjective judgment. Currently, only very few methods are available to aid in decision making. We further investigate and extend a computational concept to quantitatively assess the progression and chemical saturation of a series. To these ends, existing analogues and virtual candidates are compared in chemical space and compound neighborhoods are systematically analyzed. A large number of analogue series from different sources are studied, and alternative chemical space representations and virtual analogues of different designs are explored. Furthermore, evolving analogue series are distinguished computationally according to different saturation levels. Taken together, our findings provide a basis for practical applications of computational saturation analysis in compound optimization.

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“…The distribution of virtual analogues from SARMs can be rationalized as an envelope in chemical space formed around an existing series. 22 …”

Section: Methodsmentioning

confidence: 99%

Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions

et al. 2018

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“…Since details of the SARM methodology and matrix-based QSAR modeling have been presented in the accompanying article 6 , we initially provide only brief summaries of these methods, followed by a detailed description of the conditional probability approach.…”

Section: Methodsmentioning

confidence: 99%

“…Because the conditional probability approach is statistically grounded, prediction accuracy is expected to increase with sample sizes and matrix density 6 . Therefore, it makes sense to exclude SARMs from the calculations that contain only a small number of data set compounds or have limited row overlap (accounting for shared substitution patterns among structurally related series) 6 . Accordingly, SARMs with more than 50% row overlap are typically considered informative and prioritized for probability calculations.…”

Section: Predictions Based On Conditional Probabilities Of Activitymentioning

confidence: 99%

Follow-up: Prospective compound design using the ‘SAR Matrix’ method and matrix-derived conditional probabilities of activity

et al. 2015

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In a previous Method Article, we have presented the ‘Structure-Activity Relationship (SAR) Matrix’ (SARM) approach. The SARM methodology is designed to systematically extract structurally related compound series from screening or chemical optimization data and organize these series and associated SAR information in matrices reminiscent of R-group tables. SARM calculations also yield many virtual candidate compounds that form a “chemical space envelope” around related series. To further extend the SARM approach, different methods are developed to predict the activity of virtual compounds. In this follow-up contribution, we describe an activity prediction method that derives conditional probabilities of activity from SARMs and report representative results of first prospective applications of this approach.

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“…Asimismo, Novartis describió los principios del diseño de una biblioteca de Quimiogenómica para el blanco de familias biogénicas de GPCRs relacionados a aminas. A partir de esto, muchas publicaciones e investigaciones se han realizado para entender las interacciones de blanco-ligando y crear una matriz llamada relación estructura-actividad (SAR, por sus siglas en inglés) [12] [15].…”

Section: Antecedentesunclassified

Quimiogenómica y Quimiogenética: Definiciones, técnicas y aplicaciones en el ámbito farmacéutico

Arroyo-Rodríguez¹,

González-Young²,

Hernández-Vega³

et al. 2020

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En los años 80, tomó fuerza la ciencia dirigida al estudio del genoma humano, cuyo objetivo era mejorar la salud humana a través de la identificación de genes. Este estudio permitió el surgimiento de ciencias, entre ellas la Quimiogenómica y la Quimiogenética. La Quimiogenómica es un área interdisciplinaria que busca la sistematización del genoma al identificar, analizar, expandir y predecir las interacciones de los ligandos con las proteínas por medio de métodos in vitro e in silico. Por otro lado, la Quimiogenética se refiere al estudio de las características genéticas de un paciente para optimizar la Farmacoterapia y predecir la eficacia, los efectos secundarios y la dosificación de fármacos selectos. El conocimiento de las interacciones entre moléculas y proteínas específicas ayudaron al desarrollo de nuevas herramientas biotecnológicas y a la identificación de novedosas dianas farmacológicas que pudieran conducir a conocimientos más específicos. Algunas aplicaciones en estudio en el ámbito farmacéutico son los receptores diseñados exclusivamente activados por fármacos diseñados (DREADDs, por sus siglas en inglés). Con ellos se pretende que no se activen ante ligandos endógenos, sino en presencia de sintéticos. Actualmente existen muchas investigaciones en los cuales se estudian DREADDs para la identificación de neuronas y de vías de señalización involucradas en patologías como el miedo, la ansiedad, la depresión, la adicción y la obesidad.

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The ‘SAR Matrix’ method and its extensions for applications in medicinal chemistry and chemogenomics

Cited by 24 publications

References 13 publications

Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions

Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions

Follow-up: Prospective compound design using the ‘SAR Matrix’ method and matrix-derived conditional probabilities of activity

Quimiogenómica y Quimiogenética: Definiciones, técnicas y aplicaciones en el ámbito farmacéutico

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