---
title: "_fmcsR_: Mismatch Tolerant Maximum Common Substructure Detection for Advanced Compound Similarity Searching"
author: "Authors: Yan Wang, Tyler Backman, Kevin Horan, [Thomas Girke](mailto:thomas.girke@ucr.edu)"
date: "Last update: `r format(Sys.time(), '%d %B, %Y')`" 
package: "`r pkg_ver('fmcsR')`"
output:
  BiocStyle::html_document:
    toc: true
    toc_float: true
    toc_depth: 3
    fig_caption: yes

fontsize: 14pt
bibliography: references.bib
---
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%% \VignetteDepends{ChemmineR}
%% \VignetteKeywords{cheminformatics, structural searching, clustering}
%% \VignettePackage{fmcsR}
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<!---
- Compile from command-line
echo "rmarkdown::render('fmcsR.Rmd')" | R -slave; R CMD Stangle fmcsR.Rmd

- Commit to github
git commit -am "some edits"; git push -u origin master

- To customize font size and other style features, add this line to output section in preamble:  
    css: style.css
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```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
options(width=100, max.print=1000)
knitr::opts_chunk$set(
    eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")),
    cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")))
```

```{r setup, echo=FALSE, messages=FALSE, warnings=FALSE}
suppressPackageStartupMessages({
    library(ChemmineR)
    library(fmcsR)
})
```

Note: the most recent version of this tutorial can be found <a href="https://htmlpreview.github.io/?https://github.com/girke-lab/fmcsR/blob/master/vignettes/fmcsR.html">here</a> and a short overview slide show [here](http://faculty.ucr.edu/~tgirke/HTML_Presentations/Manuals/Workshop_Dec_5_8_2014/Rcheminfo/Cheminfo.pdf).


Introduction
============

Maximum common substructure (MCS) algorithms rank among the most
sensitive and accurate methods for measuring structural similarities
among small molecules. This utility is critical for many research areas
in drug discovery and chemical genomics. The MCS problem is a
graph-based similarity concept that is defined as the largest
substructure (sub-graph) shared among two compounds [@Wang2013a; @Cao2008a].
It fundamentally differs from the
structural descriptor-based strategies like fingerprints or structural
keys. Another strength of the MCS approach is the identification of the
actual MCS that can be mapped back to the source compounds in order to
pinpoint the common and unique features in their structures. This output
is often more intuitive to interpret and chemically more meaningful than
the purely numeric information returned by descriptor-based approaches.
Because the MCS problem is NP-complete, an efficient algorithm is
essential to minimize the compute time of its extremely complex search
process. The `fmcsR` package implements an efficient backtracking algorithm that
introduces a new flexible MCS (FMCS) matching strategy to identify MCSs
among compounds containing atom and/or bond mismatches. In contrast to
this, other MCS algorithms find only exact MCSs that are perfectly
contained in two molecules. The details about the FMCS algorithm are
described in the Supplementary Materials Section of the associated
publication [@Wang2013a]. The package provides several utilities to
use the FMCS algorithm for pairwise compound comparisons, structure
similarity searching and clustering. To maximize performance, the time
consuming computational steps of `fmcsR` are implemented in C++. Integration
with the `ChemmineR` package provides visualization functionalities of MCSs and
consistent structure and substructure data handling routines [@Cao2008c; @Backman2011a]. 
The following gives an overview of the most important functionalities provided by 
`fmcsR`.

Installation
============

The R software for running `fmcsR` and `ChemmineR` can be downloaded from CRAN
(<http://cran.at.r-project.org/>). The `fmcsR` package can be installed from an
open R session using the `BiocManager::install()` command. 
```{r eval=FALSE}
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("fmcsR") 
```


Quick Overview
==============

To demo the main functionality of the `fmcsR` package, one can load its sample
data stored as `SDFset` object. The generic `plot` function can be used to visualize the
corresponding structures. 
```{r quicktest1, eval=TRUE, fig=TRUE,fig.scap="Structures depictions of sample data." }
library(fmcsR) 
data(fmcstest)
plot(fmcstest[1:3], print=FALSE) 
```


The `fmcs` function computes the MCS/FMCS shared among two compounds, which can
be highlighted in their structure with the `plotMCS` function. 
```{r quicktest2, eval=TRUE, fig=TRUE}
test <- fmcs(fmcstest[1], fmcstest[2], au=2, bu=1) 
plotMCS(test,regenCoords=TRUE) 
```



Documentation
=============


```{r eval=TRUE, keep.source=TRUE}
library("fmcsR") # Loads the package 
```

```{r eval=FALSE, keep.source=TRUE} 
library(help="fmcsR") # Lists functions/classes provided by fmcsR 
library(help="ChemmineR") # Lists functions/classes from ChemmineR 
vignette("fmcsR") # Opens this PDF manual 
vignette("ChemmineR") # Opens ChemmineR PDF manual 
```


The help documents for the different functions and container classes can
be accessed with the standard R help syntax. 
```{r eval=FALSE, keep.source=TRUE}
?fmcs 
?"MCS-class" 
?"SDFset-class" 
```


MCS of Two Compounds
====================

Data Import
-----------

The following loads the sample data set provided by the `fmcsR` package. It
contains the SD file (SDF) of 3 molecules stored in an `SDFset` object.

```{r eval=TRUE, keep.source=TRUE}
data(fmcstest) 
sdfset <- fmcstest
sdfset 
```


Custom compound data sets can be imported and exported with the `read.SDFset`
and `write.SDF` functions, respectively. The following demonstrates this by
exporting the `sdfset` object to a file named `sdfset.sdf`. The latter is then reimported
into R with the `read.SDFset` function. 
```{r eval=FALSE, keep.source=TRUE}
write.SDF(sdfset, file="sdfset.sdf") 
mysdf <- read.SDFset(file="sdfset.sdf") 
```


Compute MCS
-----------

The `fmcs` function accepts as input two molecules provided as `SDF` or `SDFset` objects. Its
output is an S4 object of class `MCS`. The default printing behavior
summarizes the MCS result by providing the number of MCSs it found, the
total number of atoms in the query compound $a$, the total number of
atoms in the target compound $b$, the number of atoms in their MCS $c$
and the corresponding *Tanimoto Coefficient*. The latter is a widely
used similarity measure that is defined here as $c/(a+b-c)$. In
addition, the *Overlap Coefficient* is provided, which is defined as
$c/min(a,b)$. This coefficient is often useful for detecting
similarities among compounds with large size differences. 
```{r eval=TRUE, keep.source=TRUE}
mcsa <- fmcs(sdfset[[1]], sdfset[[2]]) 
mcsa 
mcsb <- fmcs(sdfset[[1]], sdfset[[3]]) 
mcsb 
```


If `fmcs` is run with `fast=TRUE` then it returns the numeric summary information in a
named `vector`. 
```{r eval=TRUE, keep.source=TRUE}
fmcs(sdfset[1], sdfset[2], fast=TRUE)
```


 Class Usage
------------

The `MCS` class contains three components named `stats`, `mcs1` and `mcs2`. The `stats` slot stores the
numeric summary information, while the structural MCS information for
the query and target structures is stored in the `mcs1` and `mcs2` slots,
respectively. The latter two slots each contain a `list` with two
subcomponents: the original query/target structures as `SDFset` objects as well
as one or more numeric index vector(s) specifying the MCS information in
form of the row positions in the atom block of the corresponding `SDFset`. A
call to `fmcs` will often return several index vectors. In those cases the
algorithm has identified alternative MCSs of equal size. 
```{r eval=TRUE, keep.source=TRUE}
slotNames(mcsa) 
```


Accessor methods are provided to return the different data components of
the `MCS` class. 
```{r eval=TRUE, keep.source=TRUE}
stats(mcsa) # or mcsa[["stats"]] 
mcsa1 <- mcs1(mcsa) # or mcsa[["mcs1"]] 
mcsa2 <- mcs2(mcsa) # or mcsa[["mcs2"]] 
mcsa1[1] # returns SDFset component
mcsa1[[2]][1:2] # return first two index vectors 
```


The `mcs2sdfset` function can be used to return the substructures stored in an
`MCS ` instance as `SDFset` object. If `type='new'` new atom numbers will be assigned to the
subsetted SDF, while `type='old'` will maintain the atom numbers from its source. For
details consult the help documents `?mcs2sdfset` and `?atomsubset`. 
```{r eval=TRUE, fig=TRUE, keep.source=TRUE}
mcstosdfset <- mcs2sdfset(mcsa, type="new")
plot(mcstosdfset[[1]], print=FALSE) 
```


To construct an `MCS` object manually, one can provide the required data
components in a `list`. 
```{r eval=TRUE, keep.source=TRUE}
mylist <- list(stats=stats(mcsa), mcs1=mcs1(mcsa), mcs2=mcs2(mcsa)) 
as(mylist, "MCS") 
```


FMCS of Two Compounds
=====================

If `fmcs` is run with its default paramenters then it returns the MCS of two
compounds, because the mismatch parameters are all set to zero. To
identify FMCSs, one has to increase the number of upper bound atom mismatches `au`
and/or bond mismatches `bu` to interger values above zero.


```{r au0bu0, eval=TRUE, fig=TRUE}
plotMCS(fmcs(sdfset[1], sdfset[2], au=0, bu=0)) 
```

```{r au1bu1, eval=TRUE, fig=TRUE}
plotMCS(fmcs(sdfset[1], sdfset[2], au=1, bu=1)) 
```


```{r au2bu2, eval=TRUE, fig=TRUE}
plotMCS(fmcs(sdfset[1], sdfset[2], au=2, bu=2)) 
```


```{r au0bu013, eval=TRUE, fig=TRUE}
plotMCS(fmcs(sdfset[1], sdfset[3], au=0, bu=0)) 
```

FMCS Search Functionality
=========================

The `fmcsBatch` function provides FMCS search functionality for compound collections
stored in `SDFset` objects. 
```{r eval=TRUE, keep.source=TRUE}
data(sdfsample) # Loads larger sample data set 
sdf <- sdfsample 
fmcsBatch(sdf[1], sdf[1:30], au=0, bu=0) 
```


Clustering with FMCS
====================

The `fmcsBatch` function can be used to compute a similarity matrix for clustering
with various algorithms available in R. The following example uses the
FMCS algorithm to compute a similarity matrix that is used for
hierarchical clustering with the `hclust` function and the result is plotted in
form of a dendrogram. 
```{r tree, eval=TRUE, fig=TRUE}
sdf <- sdf[1:7] 
d <- sapply(cid(sdf), function(x) fmcsBatch(sdf[x], sdf, au=0, bu=0, matching.mode="aromatic")[,"Overlap_Coefficient"]) 
d 
hc <- hclust(as.dist(1-d), method="complete")
plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE) 
```



The FMCS shared among compound pairs of interest can be visualized 
with `plotMCS`, here for the two most similar compounds from the previous tree:

```{r au0bu024, eval=TRUE, fig=TRUE}
plotMCS(fmcs(sdf[3], sdf[7], au=0, bu=0, matching.mode="aromatic")) 
```


Version Information
===================


```{r sessionInfo,  print=TRUE}
 sessionInfo()
```

References
===========

