Practical Applications of Matched Series Analysis: Sar Transfer, Binding Mode Suggestion and Data Point Validation

Hunt, Peter; Segall, Matthew D.; O’Boyle, Noel M.; Sayle, Roger A.

doi:10.4155/fmc-2016-0203

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…The complete leave-one-out MMS analysis of Pfizer’s database showed that more than one-third of the 1 million random predictions achieved a strong correlation with potency ( R 2 > 0.49), further indicating that this method can greatly facilitate MMS for scaffold replacement and optimization. Based on the combination of MMS network and structural graph, Hunt et al observed that MMS analysis provides more detailed information on compound alignments or their binding modes to be explored, providing possible hypotheses for further lead optimization …”

Section: Practical Applications Of Mmpamentioning

confidence: 99%

“…Based on the combination of MMS network and structural graph, Hunt et al observed that MMS analysis provides more detailed information on compound alignments or their binding modes to be explored, providing possible hypotheses for further lead optimization. 124 MMPA Public Tools: Web Servers, Software, and Databases. To better curate and exchange knowledge on drug hunting, several MMPA tools have been developed to facilitate the analysis of the relationship between chemical structures and biological effects or physiochemical properties, thus accelerating the design of new drugs.…”

Section: ■ Practical Applications Of Mmpamentioning

confidence: 99%

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Matched Molecular Pair Analysis in Drug Discovery: Methods and Recent Applications

Yang

Shi

et al. 2023

J. Med. Chem.

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Matched molecular pair analysis (MMPA) is a tool to extract knowledge of medicinal chemistry from existing experimental data, and it relates changes in activities or properties to specific structural changes. More recently, MMPA has also been applied in multi-objective optimization and de novo drug design. Here, we discuss the concept, techniques, and case studies of MMPA, which provide an overview of the current development in the field of MMPA. This Perspective also summarizes up-to-date MMPA applications and highlights the successes and opportunities for further MMPA advances.

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Section: Practical Applications Of Mmpamentioning

confidence: 99%

Section: ■ Practical Applications Of Mmpamentioning

confidence: 99%

Matched Molecular Pair Analysis in Drug Discovery: Methods and Recent Applications

Yang

Shi

et al. 2023

J. Med. Chem.

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“…Matched molecular series analysis (MMSA) has emerged as a new computational data analysis approach to support the compound optimization process. − Specifically, it attempts to answer the question, “what other R-groups are worth evaluating in analogue series?” Wawer and Bajorath first introduced the concept of matched molecular series (MMS) as an extension of matched molecular pair analysis − (MMPA, Figure a) to capture structure–activity relationship (SAR) information from large compound databases. Since then, MMSA has been studied extensively in the context of SAR transfer − as an idea generation ,, and semiquantitative activity prediction tool , but not yet for ADMET property prediction.…”

Section: Introductionmentioning

confidence: 99%

Matched Molecular Series Analysis for ADME Property Prediction

Awale

Riniker

Krämer

2020

J. Chem. Inf. Model.

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Generation and prioritization of new molecules are the most central part of the drug design process. Matched molecular series analysis (MMSA) has recently been proposed as a formal approach that captures both of these key elements of design. In order to better understand the power of MMSA and its specific limitations, we here evaluate its performance as an ADME property prediction tool. We use four large and diverse inhouse data sets, logD, microsomal clearance, CYP2C9, and CYP3A4 inhibition. MMSA follows the concept of parallel structure–activity relationship (SAR), where if two identical substituent series on different scaffolds show similarity in their property profiles, SAR from one series can be transferred to the other series. We test four different similarity metrics to identify pairs of molecular series where information can be transferred. We find that the best prediction performance is achieved by a combination of centered root-mean-square deviation (cRMSD) and a network score approach previously published by Keefer et al. However, cRMSD alone strikes the best balance between accuracy and the number of predictions that can be made. We identify statistical metrics that allow estimating when MMSA predictions will work, similar to the well-known applicability domain concept in machine learning. MMSA achieves a prediction accuracy that is comparable to a standard machine-learning model and matched molecular pair analysis. In contrast to machine learning, however, it is very easy to understand where MMSA predictions are coming from. Finally, to prospectively test the power of MMSA, we retested compounds that were strong outliers in the initial predictions and show how the MMSA model can help to identify erroneous data points.

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“…With the aid of structural data and models such as those listed below, the reason for discontinuities can be investigated and evaluated. Finding and explaining activity cliffs with the inclusion of structural data into quantitative structure–activity relationship (QSAR) models, SAR transfer between molecular series, and visualization of activity patterns as well as high‐impact sites are some of the main applications of the 3D MMP concept. Depending on how 3D similarity is defined, scaffold hopping can be realized as well …”

Section: Introductionmentioning

confidence: 99%

Exploring Structure–Activity Relationships with Three‐Dimensional Matched Molecular Pairs—A Review

Ehmki

Rarey

2018

ChemMedChem

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A matched molecular pair (MMP) consists of two small molecules that differ by a few atoms only. The minor structural difference between the molecules allows a detailed analysis of changes in properties. Three-dimensional (3D) MMPs extend the concept of chemical similarity by spatial similarity. Conformations must be generated, and superimpositions have to be calculated. The additional complexity and uncertainty as well as the smaller amount of available experimental data substantially complicates the derivation of models. Nonetheless, there are some benefits that make the transition worthwhile. The 3D concept gives detailed insight into mechanisms behind several methods classically used by the 2D MMP approach. It can help to analyze disrupted series of structure-activity relationships or extend the 2D MMP concept with scaffold hopping. One of the most powerful features is the high confidence structure-activity relationship transfer between series of analogues. Several research groups have approached the problem from different directions. The models vary especially in the 3D similarity measure used and complexity of the applied descriptor selected or designed. Nonetheless, all approaches have increased the amount of information available by incorporating 3D structural information.

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Practical Applications of Matched Series Analysis: Sar Transfer, Binding Mode Suggestion and Data Point Validation

Cited by 5 publications

References 17 publications

Matched Molecular Pair Analysis in Drug Discovery: Methods and Recent Applications

Matched Molecular Pair Analysis in Drug Discovery: Methods and Recent Applications

Matched Molecular Series Analysis for ADME Property Prediction

Exploring Structure–Activity Relationships with Three‐Dimensional Matched Molecular Pairs—A Review

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