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Matsy

Tools for Matched Series Analysis [v1.2.1 Mar-2024]

A pair of molecules are Matched Molecular Pairs if they differ in structure at a single substitutable position; that is, they have the same scaffold but different R groups. A Matched Molecular Series generalises the concept of Matched Pairs to N molecules (N >= 2) that share the same scaffold but have different R groups at the same position.

While Matched Pair analysis has had some success particularly in the area of physical property prediction, it has been less successful for biological activity prediction. In recent work published in J. Med. Chem. [1], in collaboration with AstraZeneca we have shown that longer Matched Series are much better at predicting R groups likely to improve activity.

Matsy (short for "Matched Series") is a set of software tools to generate and search a database of matched series, and to use the database to predict R groups most likely to increase activity (or some other property) further given a particular observed order of R groups so far. An overview of the predictive method is shown below.

Matsy Overview

References

1. O'Boyle, N. M.; Boström, J.; Sayle, R. A.; Gill, A. Using Matched Molecular Series as a Predictive Tool To Optimize Biological Activity. Journal of Medicinal Chemistry. 2014, 57, 2704. [Open Access]

Further info

A presentation on the visualization and manipulation of Matched Molecular Series for decision support presented at the ACS meeting in Boston, August 2015 [PDF]
A presentation describing how to update the Topliss decision tree using Matched Molecular Series data presented at the ACS meeting in San Francisco, August 2014 [PDF]
A presentation giving an overview of the Matsy algorithm, "Evidence-based medicinal chemistry", presented at the UK QSAR meeting in Cambridge (UK), Sept 2014 [PDF]

The following video demonstrates using the Matsy GUI to check Topliss Tree predictions:

Arthor provides fast state-of-the-art substructure and chemical similarity search capabilities for ultra-large databases of hundreds of millions of compounds, using SMARTS optimization, Just-In-Time compilation and/or GPUs.

CaffeineFix is used to rapidly match chemical names or terms against a dictionary or grammar (e.g. a grammar for IUPAC names). As well as use in text-mining, it can be used to provide autocomplete functionality and spell-correction.

Casandra is a server for delivering real time safety warnings of experimental hazards straight to the pharmaceutical electronic laboratory notebooks (ELNs).

HazELNut is a suite of tools used to extract, normalize and analyse information in Electronic Lab Notebooks (ELNs). This can be used to implement a search interface, find/eliminate duplicates, find similar reactions and so on.

LeadMine extracts chemical names and terms from text. It incorporates NextMove's CaffeineFix technology to find terms that match appropriate dictionaries or grammars. It has enhanced functionality to handle the patent literature.

Matsy is a set of tools for creating and analysing Matched Molecular Series (the general form of Matched Molecular Pairs). In particular, it can be used to suggest what compound to make next in a Medicinal Chemistry program.

MPSearch rapidly searches a database to find Matched Pairs related to a query molecule. This type of search is used to explore previous medicinal chemistry strategies.

NameRXN is used to classify and name reactions. It is particular useful in the context of ELN analysis but also as a plugin to chemical drawing software. NameRXN builds on NextMove Software's Patsy technology.

Patsy is used to speed up SMARTS pattern matching by creating optimized SMARTS patterns or source code. Speed gains are particularly large when multiple SMARTS patterns are matched against a single structure.

Pistachio is a reaction dataset browser providing loading, querying, and analytics of chemical reactions. With over 9 million chemical reactions extracted from US & EPO patents, it demonstrates an AI interface to faceted (structure) search

SmallWorld is an index of chemical space based on more than 230 billion molecular substructures. It can be used to measure similarity based on graph-edit distance, find the MCS of two or more molecules, analyse HTS results and much more.

Sugar & Splice can be used to perceive and depict biopolymer structure. It makes it easy to interconvert between small-molecule representations (e.g. SMILES, MOL) and biopolymer representations (HELM, IUPAC line notation).