Package {admetshiny}

Type:	Package
Title:	Interactive ADMET and Drug-Likeness Analysis of Small Molecules
Version:	1.0.0
Date:	2026-08-29
Maintainer:	Xavier Clemente Garcia Cevallos <xgarcia@unicauca.edu.co>
Description:	Provides an interactive Shiny application and a toolbox of R functions for the management, calculation, filtering, visualization and exploratory analysis of molecular descriptors and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) properties of small molecules. Computes descriptors locally via the Chemistry Development Kit (CDK), and offers drug-likeness filters (Lipinski, Veber, Ghose, Egan, Muegge), the BOILED-Egg model for gastrointestinal absorption and blood-brain barrier permeability, a P-glycoprotein (P-gp, also known as ATP-binding cassette sub-family B member 1, ABCB1) substrate Random Forest classifier, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), radar plots and Tanimoto / AGglomerative NESting (AGNES) clustering to support compound prioritization in early-stage drug discovery.
License:	MIT + file LICENSE
URL:	https://github.com/xavierclementegarcia/admetshiny
BugReports:	https://github.com/xavierclementegarcia/admetshiny/issues
Depends:	R (≥ 3.5.0)
Imports:	cluster, dplyr, DT, fingerprint, fmsb, GGally, ggplot2, ggrepel, graphics, grDevices, grid, magrittr, openxlsx, rcdk, rmarkdown, Rtsne, shiny, stats, tools, utils, uwot, viridisLite, webchem
Suggests:	knitr, testthat
VignetteBuilder:	knitr
Config/roxygen2/version:	8.1.0
Encoding:	UTF-8
SystemRequirements:	Java (>= 8); only required for the optional CDK-based descriptor calculation (rcdk) module.
NeedsCompilation:	no
Packaged:	2026-09-22 06:11:21 UTC; XavierPC
Author:	Xavier Clemente Garcia Cevallos [aut, cre]
Repository:	CRAN
Date/Publication:	2026-09-30 11:30:02 UTC

admetshiny: Interactive ADMET and Drug-Likeness Analysis

Description

The admetshiny package provides an interactive Shiny application and a collection of R functions for the management, calculation, filtering, visualization and exploratory analysis of molecular descriptors and ADMET properties of small molecules. It computes descriptors locally via the Chemistry Development Kit (CDK) through rcdk, and offers drug-likeness filters (Lipinski, Veber, Ghose, Egan, Muegge), the BOILED-Egg model, a P-glycoprotein substrate Random Forest classifier, PCA, t-SNE, UMAP, parallel coordinates, radar plots and Tanimoto/AGNES structural similarity clustering.

Details

To launch the interactive application simply run:

admetshiny::run_app()

The exported functions (filters, plots, data normalisation and CDK helpers) can also be used programmatically in plain R scripts. By Xavier Clemente Garcia Cevallos :3 "Hecho con amor para la ciencia"

Drug-likeness filters

• lipinskiFilter - Lipinski Rule-of-Five

• veberFilter - Veber oral bioavailability

• ghoseFilter - Ghose qualifying range

• eganFilter - Egan PSA-LogP rule

• mueggeFilter - Muegge pharmacophore filter

• applyFilters - Apply multiple filters in sequence

• computeViolationColumns - Compute violation columns

Data normalization

• mapADMETColumns - Map any user dataset to standard schema

• mapCDKDescriptors - Map CDK descriptors to standard schema

CDK & webchem

• getSmilesFromIdentifiers - Retrieve SMILES from PubChem

• calcCDKDescriptors - Calculate descriptors with CDK

• computeADMETProperties - Compute BOILED-Egg ADMET properties

Visualizations

• plotBoiledEgg - BOILED-Egg model

• plotMW, plotTPSA, plotLogP - Distributions

• plotPCA - PCA chemical space

• plotTSNE - t-SNE chemical space

• plotUMAP - UMAP chemical space

• plotParallel - Parallel coordinates

• plotViolin - Violin plot

• plotRadar - Radar chart

• plotTanimoto - Tanimoto/AGNES clustering

• plotCorrHeatmap - Correlation heatmap

• plotClusterHeatmap - Cluster heatmap with dendrogram

• plotHistogramCustom - Customizable histogram of any numeric column

• palette_selector_ui, apply_palette - Colour customization

Application

• run_app - Launch the Shiny application

• generateReport - Generate a report from the R console

Author(s)

Maintainer: Xavier Clemente Garcia Cevallos xgarcia@unicauca.edu.co (ORCID)

Authors:

• Xavier Clemente Garcia Cevallos xgarcia@unicauca.edu.co (ORCID)

See Also

Useful links:

• https://github.com/xavierclementegarcia/admetshiny

• Report bugs at https://github.com/xavierclementegarcia/admetshiny/issues

Pipe operator

Description

Re-exports %>% from magrittr so that the pipe can be used with admetshiny::%>% or after attaching the package.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs, ...).

Check that required columns exist before filtering

Description

Internal helper used by every drug-likeness filter to fail early with an informative message when an expected column is missing from the input data.

Usage

.checkColumns(data, required_columns, filter_name)

Arguments

Value

Invisible NULL; called for its side-effect of stopping when columns are missing.

Safely extract a numeric column

Description

Safely extract a numeric column

Usage

.get_col(data, col)

Get heatmap colour palette

Description

Internal helper that returns a colour vector for the cluster heatmap based on the selected palette name.

Usage

.get_heatmap_palette(palette_name)

Arguments

Value

A character vector of colour hex codes.

Build a Markdown pipe table from a data.frame or matrix

Description

Build a Markdown pipe table from a data.frame or matrix

Usage

.md_table(df)

Summary statistics for a numeric vector

Description

Summary statistics for a numeric vector

Usage

.numeric_summary(x)

Safe rbind that filters out NULL entries

Description

do.call(rbind, ...) fails with "'dimnames' applied to non-array" when the list contains NULL entries. This helper removes them first.

Usage

.safe_rbind(list_of_dfs)

About tab UI

Description

Builds the About tab of the ADMETShiny application.

Usage

about_tab()

Value

A shiny.tabPanel.

ADMET Master Manager tab UI

Description

Builds the "ADMET Master Manager" tab of the ADMETShiny application. This module is a four-step wizard (Upload & Preview -> Map Columns -> Filter -> Plots) that lets users bring in any tabular dataset (CSV or Excel) and map its columns to the application's standard schema, so that the same drug-likeness filters and plots as the other modules can be applied to data coming from any source.

Usage

admet_master_tab()

Value

A shiny.tabPanel.

See Also

mapADMETColumns, detectColumnTypes, detectSMILESColumn.

Package version and codename

Description

Internal constants used across the Shiny application (Home and About tabs) to display the current package version and its botanical codename.

Usage

ADMETSHINY_VERSION

ADMETSHINY_CODENAME

Format

Length-one character vectors.

ADMETShiny application server

Description

The server function backing the ADMETShiny Shiny application. It is not meant to be called directly by end users; use run_app to launch the app.

Usage

app_server(input, output, session)

Arguments

ADMETShiny application UI

Description

Builds the full Shiny UI for the ADMETShiny application: a collapsible navbarPage with the Home, CDK & webchem, ADMET Master Manager, Report, Documentation, Tutorial and About tabs, plus the floating dark-mode toggle button.

Usage

app_ui()

Details

This function is intended for internal use; end users should launch the app with run_app.

Value

A shiny.tag.list suitable for shiny::shinyApp().

Apply drug-likeness filters in sequence

Description

Applies the selected drug-likeness filters to a normalized data.frame. The filters are applied in the order Lipinski, Veber, Ghose, Egan, Muegge; only those named in filters are executed.

Usage

applyFilters(
  data,
  filters,
  lipinski = list(),
  veber = list(),
  ghose = list(),
  egan = list(),
  muegge = list()
)

Arguments

Value

A data.frame with the rows of data that pass every selected filter.

See Also

lipinskiFilter, veberFilter, ghoseFilter, eganFilter, mueggeFilter.

Examples

d <- data.frame(MW = 300, LogP = 2, TPSA = 80, MR = 90,
  "#Heavy atoms" = 22, "#Rotatable bonds" = 3,
  "#H-bond acceptors" = 4, "#H-bond donors" = 2,
  "Lipinski #violations" = 0, "Ghose #violations" = 0,
  "Veber #violations" = 0, "Egan #violations" = 0,
  "Muegge #violations" = 0, check.names = FALSE)
applyFilters(d, filters = c("Lipinski", "Veber", "Egan"))

Apply a colour palette to a ggplot object

Description

Modifies an existing ggplot object by adding a colour/fill scale matching the selected palette, intelligently detecting whether the colour variable is continuous or discrete.

Usage

apply_palette(p, palette_name, data, color_col)

Arguments

Value

A ggplot object (possibly modified).

Examples

library(ggplot2)
d <- data.frame(MW = c(300, 400, 500), group = c("A", "A", "B"))
p <- ggplot(d, aes(x = group, y = MW, color = group)) + geom_point()
p <- apply_palette(p, "Set1", d, "group")

Build the full Markdown report content

Description

Build the full Markdown report content

Usage

buildReportMarkdown(datasets, plot_paths)

Arguments

Value

A character scalar with the full Markdown document.

Calculate molecular descriptors with CDK

Description

Parses SMILES strings and calculates the requested molecular descriptors locally using the Chemistry Development Kit (CDK) through the suggested package rcdk. Requires a working Java JDK (a JRE alone is not sufficient).

Usage

calcCDKDescriptors(
  smiles,
  which = c("mw", "alogp", "tpsa", "hbd", "hba", "rotb", "heavy", "aroma", "mr")
)

Arguments

Value

A data.frame with one row per valid molecule and a SMILES column.

See Also

mapCDKDescriptors, getSmilesFromIdentifiers.

Examples

smiles <- c("CCO", "CC(=O)Oc1ccccc1C(=O)O")
desc <- calcCDKDescriptors(smiles)

CDK & webchem tab UI

Description

Builds the CDK & webchem tab of the ADMETShiny application.

Usage

cdk_tab()

Value

A shiny.tabPanel.

Compute BOILED-Egg ADMET properties

Description

Computes the gastrointestinal absorption (GI absorption), blood-brain barrier permeability (BBB permeant) and P-glycoprotein substrate (Pgp substrate) categorical columns from the LogP and TPSA values, using the official BOILED-Egg polygon coordinates (Daina & Zoete, 2016, Data S3) for GI/BBB classification via point-in-polygon testing, and a Random Forest classifier for P-gp substrate prediction.

Usage

computeADMETProperties(data)

Arguments

Details

The BOILED-Egg model was originally calibrated with WLOGP; here the application's generic LogP column is used (which may be WLOGP, Consensus Log P, ALogP, or the source platform's own LogP). This is an acceptable approximation for exploratory visualization.

Value

A data.frame with the added ADMET property columns.

References

Daina, A., & Zoete, V. (2016). A boiled egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 11(11), 1117-1121.

Sedykh, A., Fourches, D., Duan, J., et al. (2013). Human intestinal transporter database: QSAR modeling and virtual excretion experiments. J. Cheminformatics 2015, 7:21 (Metrabase P-gp data).

Examples

d <- data.frame(LogP = 2, TPSA = 80, MW = 300,
  "#H-bond donors" = 2, "#H-bond acceptors" = 4, check.names = FALSE)
d <- computeADMETProperties(d)

Compute additional literature-supported drug-likeness metrics

Description

Compute additional literature-supported drug-likeness metrics

Usage

computeAdditionalMetrics(data)

Arguments

Value

A data.frame with metric name, \

Compute per-dataset statistics for the report

Description

Compute per-dataset statistics for the report

Usage

computeDatasetStats(data_raw, data_filtered, filters_applied, source_name)

Arguments

Value

A list with all computed statistics.

Compute a composite drug-likeness score (0-100)

Description

For each compound, counts how many of the five drug-likeness rules (Lipinski, Ghose, Veber, Egan, Muegge) have zero violations, then scales to 0-100. A compound passing all five rules scores 100; one passing none scores 0.

Usage

computeDruglikenessScore(data)

Arguments

Value

A list with per-compound scores and summary statistics.

Compute drug-likeness violation columns

Description

Computes the five drug-likeness "#violations" columns (Lipinski, Ghose, Veber, Egan, Muegge) from the physicochemical properties, using the thresholds from the original publications. Missing columns are safely filled with NA.

Usage

computeViolationColumns(data)

Arguments

Details

The generic LogP column is used for all LogP-dependent rules. The original publication thresholds are used:

• Lipinski: LogP > 5 (original Rule-of-Five)

• Ghose: LogP < -0.4 or LogP > 5.6

• Egan: LogP > 5.88

• Muegge: LogP < -2 or LogP > 5

For the Ghose atom-count criterion (20-70 atoms), the function uses "#Total atoms" (heavy + H, matching the original Ghose 1999 definition) when available, and falls back to "#Heavy atoms" for backwards compatibility.

The Veber violation is binary: a compound violates if it has more than 10 rotatable bonds OR if both polarity conditions fail (TPSA > 140 AND HBA + HBD > 12).

Value

A data.frame with the added violation columns.

See Also

lipinskiFilter, ghoseFilter, veberFilter, eganFilter, mueggeFilter.

Examples

d <- data.frame(MW = 300, LogP = 2, TPSA = 80, MR = 90,
  "#H-bond acceptors" = 4, "#H-bond donors" = 2,
  "#Rotatable bonds" = 3, "#Heavy atoms" = 22, check.names = FALSE)
d <- computeViolationColumns(d)

Dark mode UI module

Description

Returns the UI elements required for the dark-mode toggle: the CSS rules for body.dark-mode, the JavaScript custom message handler and the floating toggle button.

Usage

dark_mode_ui()

Value

A shiny.tag.list with the CSS, script and action button.

Detect column types in a data.frame

Description

Returns a compact summary of every column in a data.frame, with its inferred type (numeric / string), the number of unique non-NA values and a small sample of the first few values. Used by the ADMET Master Manager to help the user pick the right column mapping.

Usage

detectColumnTypes(data)

Arguments

Value

A data.frame with columns: column_name, detected_type, n_unique, sample_values.

See Also

detectSMILESColumn, mapADMETColumns.

Auto-detect SMILES column in a data.frame

Description

Tries to identify the column that contains SMILES strings. It first looks for column names matching common conventions (smiles, SMILES, CanonicalSMILES, canonical_smiles, IsomericSMILES, etc.). If no name matches, it inspects the values of every string column and picks the one whose values look most like SMILES (contain carbon / aromatic / bracket / bond characters).

Usage

detectSMILESColumn(data)

Arguments

Value

Character name of the detected SMILES column, or NULL if none found.

See Also

detectColumnTypes, mapADMETColumns.

Documentation tab UI

Description

Builds the Documentation tab of the ADMETShiny application.

Usage

docs_tab()

Value

A shiny.tabPanel.

Egan drug-likeness filter

Description

Filters compounds according to the Egan rule (Egan et al., 2000) based on TPSA and LogP.

Usage

eganFilter(data, e_tpsa = 131.6, e_logp = 5.88, violations = 0)

Arguments

Value

A data.frame with the rows of data that satisfy all thresholds.

References

Egan, W. J., Merz, K. M., & Baldwin, J. J. (2000). Prediction of drug absorption using multivariate statistics. Journal of Medicinal Chemistry, 43(21), 3867-3877.

Examples

d <- data.frame(TPSA = 80, LogP = 2, "Egan #violations" = 0,
  check.names = FALSE)
eganFilter(d)

Generate an ADMETShiny report from the R console

Description

Generates a comprehensive ADMET and drug-likeness analysis report from one or more datasets, directly from the R console (without launching the Shiny app). The report includes per-dataset statistics, drug-likeness filter results, BOILED-Egg ADMET classification, additional literature-supported metrics, a composite drug-likeness score, cross-dataset comparison, visualizations and references.

Usage

generateReport(
  data,
  filters = character(0),
  format = "html",
  output_file = NULL,
  source_name = "Dataset"
)

Arguments

Value

Invisible NULL; called for the side-effect of writing the rendered report to output_file.

Examples

## Build a small synthetic ADMET dataset in the standard schema
d <- data.frame(
  Name = c("druglike", "violator"),
  SMILES = c("CCO", "CCCCCCCCCCCCCCCCCCCCCCCCCC"),
  MW = c(300, 650),
  LogP = c(2, 7),
  TPSA = c(40, 160),
  MR = c(70, 150),
  "#H-bond acceptors" = c(4, 12),
  "#H-bond donors" = c(2, 7),
  "#Rotatable bonds" = c(3, 14),
  "#Heavy atoms" = c(20, 80),
  "#Aromatic heavy atoms" = c(6, 9),
  check.names = FALSE
)
d <- computeViolationColumns(d)

## Generate an HTML report (written to a temp file)
out <- tempfile(fileext = ".html")
generateReport(d, filters = c("Lipinski", "Veber"),
               format = "html", output_file = out,
               source_name = "Example")

Generate and save all plots for a dataset as PNG files

Description

Generate and save all plots for a dataset as PNG files

Usage

generateReportPlots(data, prefix, plot_dir)

Arguments

Value

A named list of plot file paths.

Retrieve canonical SMILES from PubChem

Description

Queries PubChem (via the suggested package webchem) to obtain CIDs and canonical SMILES for a vector of chemical identifiers (names, CAS numbers, InChIKeys or PubChem CIDs).

Usage

getSmilesFromIdentifiers(ids, from = "name")

Arguments

Value

A data.frame with columns query, cid, CanonicalSMILES, IsomericSMILES, MolecularFormula, IUPACName.

See Also

calcCDKDescriptors.

Examples

## Not run: 
# This service requires a constant internet connection and may fail if the
# server goes down. It may also involve long wait times. That's the main
# reason for using dontrun in this case.
ids <- c("aspirin", "ibuprofen")
smiles <- getSmilesFromIdentifiers(ids, from = "name")

## End(Not run)

Ghose drug-likeness filter

Description

Filters compounds according to the Ghose qualifying range (Ghose et al., 1999): molecular weight, molar refractivity, LogP and number of atoms.

Usage

ghoseFilter(
  data,
  g_mw_min = 160,
  g_mw_max = 480,
  g_mr_min = 40,
  g_mr_max = 130,
  g_logp_min = -0.4,
  g_logp_max = 5.6,
  g_ha_min = 20,
  g_ha_max = 70,
  violations = 0
)

Arguments

Details

The "number of atoms" criterion (range 20-70) refers to TOTAL atoms (heavy + hydrogens), following the original Ghose (1999) definition.

Value

A data.frame with the rows of data that satisfy all thresholds.

References

Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of Combinatorial Chemistry, 1(1), 55-68.

Examples

d <- data.frame(MW = 300, MR = 90, LogP = 2, "#Total atoms" = 30,
  "Ghose #violations" = 0, check.names = FALSE)
ghoseFilter(d)

Home tab UI

Description

Builds the Home (landing) tab of the ADMETShiny application.

Usage

home_tab()

Value

A shiny.tabPanel.

Shared info-card CSS

Description

Returns a tags$head element with the CSS rules for the .info-card family of classes used across the application, including dark-mode variants.

Usage

info_card_css()

Value

A shiny.tag.

Lipinski Rule-of-Five filter

Description

Filters compounds according to the Rule of Five (Lipinski et al., 1997): molecular weight, LogP, number of H-bond acceptors and donors, plus the pre-computed number of Lipinski violations.

Usage

lipinskiFilter(data, mw = 500, logp = 5, hba = 10, hbd = 5, violations = 0)

Arguments

Details

Uses the generic LogP column with the original threshold of 5.

Value

A data.frame with the rows of data that satisfy all thresholds.

References

Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23(1-3), 3-25.

Examples

d <- data.frame(MW = 300, LogP = 2, "#H-bond acceptors" = 4,
  "#H-bond donors" = 2, "Lipinski #violations" = 0, check.names = FALSE)
lipinskiFilter(d)

Map user columns to standard ADMET schema

Description

Takes a raw data.frame and a user-specified column mapping, renames columns to the application's standard schema, converts types, optionally calculates missing descriptors with CDK, and computes violation columns and ADMET properties.

Usage

mapADMETColumns(data, mapping, calculate_cdk = TRUE)

Arguments

Details

The mapping argument is a named character vector where each name is the name of a column in data and each value is one of the standard short field codes used by the ADMET Master Manager: "None", "SMILES", "Name", "MW", "LogP", "WLOGP", "TPSA", "HBD", "HBA", "Rotatable Bonds", "Molar Refractivity", "Heavy Atoms", "Aromatic Heavy Atoms", "GI Absorption", "GI Absorption_num", "BBB Permeant", "BBB Permeant_num", "Pgp Substrate", "Pgp Substrate_num", "LogS", "LogD".

Value

A data.frame with standardized column names, violation columns, and ADMET properties.

See Also

computeViolationColumns, computeADMETProperties, calcCDKDescriptors.

Map CDK descriptors to the application's standard schema

Description

Translates the raw CDK descriptor names into the application's canonical column names and computes the drug-likeness violation columns and the BOILED-Egg ADMET properties (GI absorption, BBB permeability, P-gp substrate).

Usage

mapCDKDescriptors(cdk_df)

Arguments

Details

The CDK ALogP descriptor is mapped to the generic LogP column. No artificial MLOGP/WLOGP/XLOGP3 columns are created; the application uses a single LogP column for all drug-likeness filters, with thresholds from the original publications.

Value

A data.frame with renamed columns, violation columns and ADMET properties.

See Also

calcCDKDescriptors, computeViolationColumns, computeADMETProperties.

Examples

smiles <- c("CCO", "CC(=O)Oc1ccccc1C(=O)O")
desc <- calcCDKDescriptors(smiles)
mapped <- mapCDKDescriptors(desc)

Muegge drug-likeness filter

Description

Filters compounds according to the Muegge pharmacophore-point filter (Muegge et al., 2001): MW, LogP, HBA, HBD, TPSA and rotatable bonds.

Usage

mueggeFilter(
  data,
  m_mw_min = 200,
  m_mw_max = 600,
  m_logp_min = -2,
  m_logp_max = 5,
  m_hba = 10,
  m_hbd = 5,
  m_rb = 15,
  m_tpsa = 150,
  violations = 0
)

Arguments

Value

A data.frame with the rows of data that satisfy all thresholds.

References

Muegge, I., Heald, S. L., & Brittelli, D. (2001). Simple selection criteria for drug-like chemical matter. Journal of Medicinal Chemistry, 44(12), 1841-1846.

Examples

d <- data.frame(MW = 300, LogP = 2, TPSA = 80,
  "#H-bond acceptors" = 4, "#H-bond donors" = 2,
  "#Rotatable bonds" = 3, "Muegge #violations" = 0, check.names = FALSE)
mueggeFilter(d)

Colour palette selector UI element

Description

A Shiny selectInput that lets the user choose a colour palette for the plots produced by the application.

Usage

palette_selector_ui(id)

Arguments

Value

A shiny.tag (select input).

Examples

library(shiny)
palette_selector_ui("my_palette")

BOILED-Egg plot

Description

Produces the BOILED-Egg model plot (Daina & Zoete, 2016) using the official polygon coordinates from the supplementary data (Data S3). Points are coloured by P-gp substrate status when a "Pgp substrate" column is available.

Usage

plotBoiledEgg(data, logp_source = NULL)

Arguments

Details

The BOILED-Egg model was originally calibrated with WLOGP; the application uses the generic LogP column as an approximation. The axes are TPSA on the x-axis and LogP on the y-axis.

Value

A ggplot2 object.

References

Daina, A., & Zoete, V. (2016). A boiled egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 11(11), 1117-1121.

Examples

d <- data.frame(LogP = c(2, 5), TPSA = c(50, 150))
plotBoiledEgg(d)

Cluster heatmap with dendrogram

Description

Produces a heatmap of physicochemical properties with a dendrogram coupled to the left side (compounds) showing hierarchical clustering. The properties (columns) are also clustered and the column dendrogram appears on top. Values are z-score standardized per property.

Usage

plotClusterHeatmap(
  data,
  variables = NULL,
  id_col = NULL,
  method = "ward.D2",
  scale_data = TRUE,
  palette = "default"
)

Arguments

Details

The plot is rendered using base R graphics so it works without additional dependencies beyond what the package already requires.

Value

Invisible NULL; called for the side-effect of drawing the plot on the current graphics device.

References

Murtagh, F., & Contreras, P. (2012). Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1), 86-97.

Examples

d <- data.frame(
  Name = c("A", "B", "C", "D"),
  MW = c(300, 350, 600, 320),
  LogP = c(2, 3, 5, 1),
  TPSA = c(50, 60, 120, 40),
  check.names = FALSE
)
plotClusterHeatmap(d, id_col = "Name")

Correlation heatmap of physicochemical properties

Description

Produces a heatmap of the Pearson correlation between key physicochemical properties.

Usage

plotCorrHeatmap(
  data,
  props = c("MW", "LogP", "TPSA", "MR", "#H-bond acceptors", "#H-bond donors",
    "#Rotatable bonds", "#Heavy atoms")
)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(MW = c(300, 400, 500), LogP = c(2, 3, 4),
  TPSA = c(60, 80, 100))
plotCorrHeatmap(d)

Drug-likeness composite score distribution

Description

Produces a histogram of the composite drug-likeness score (0-100) with color-coded ranges: poor (<60), acceptable (60-79), excellent (>=80).

Usage

plotDruglikenessScore(data)

Arguments

Value

A ggplot2 object.

Custom histogram

Description

Produces a highly customizable histogram of any numeric column in the dataset, with optional grouping (overlaid), kernel density overlay, rug plot, and configurable number of bins. This complements the pre-defined plotMW, plotTPSA and plotLogP histograms by allowing the user to pick any numeric variable (including user-supplied columns from CSV uploads, ADMET probabilities, etc.).

Usage

plotHistogramCustom(
  data,
  variable,
  bins = 30,
  group_by = "None",
  show_density = TRUE,
  show_rug = FALSE
)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(MW = c(300, 400, 500, 350, 450, 380),
  group = c("A", "A", "B", "B", "C", "C"))
plotHistogramCustom(d, variable = "MW", group_by = "group")

LogP distribution histogram

Description

LogP distribution histogram

Usage

plotLogP(data)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(LogP = c(1, 2, 3))
plotLogP(d)

Molecular weight distribution histogram

Description

Molecular weight distribution histogram

Usage

plotMW(data)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(MW = c(300, 400, 500))
plotMW(d)

Principal Component Analysis (PCA) chemical space plot

Description

Computes a PCA on the selected numeric physicochemical variables and produces a scatterplot of the first two principal components, optionally coloured by a grouping variable, with 95% confidence ellipses, observation labels and variable loading arrows. Labels require the suggested package ggrepel.

Usage

plotPCA(
  data,
  variables = NULL,
  color_by = "None",
  label_by = "None",
  scale_data = TRUE,
  ellipse = TRUE
)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(MW = c(300, 400, 500), LogP = c(2, 3, 4),
  TPSA = c(60, 80, 100), group = c("A", "A", "B"))
plotPCA(d, variables = c("MW", "LogP", "TPSA"), color_by = "group")

Parallel coordinates plot

Description

Produces a parallel coordinates plot of the selected numeric variables, optionally coloured by a grouping variable. Requires the suggested package GGally.

Usage

plotParallel(data, variables, color_by = NULL, scale_data = TRUE)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(MW = c(300, 400, 500), LogP = c(2, 3, 4),
  TPSA = c(60, 80, 100), group = c("A", "A", "B"))
plotParallel(d, variables = c("MW", "LogP", "TPSA"), color_by = "group")

Radar plot of physicochemical profile

Description

Compares up to 5 molecules simultaneously on a radar chart using 6 key physicochemical properties, normalized 0-1 against the range observed in the full dataset. Requires the suggested package fmsb.

Usage

plotRadar(
  data,
  id_col,
  ids,
  props = c("MW", "LogP", "TPSA", "#H-bond acceptors", "#H-bond donors",
    "#Rotatable bonds")
)

Arguments

Value

Invisibly NULL; called for the side-effect of drawing the radar chart on the current graphics device.

References

Nakazawa, M. (2019). fmsb: Functions for Medical Statistics Book with some Demographic Data. R package.

Examples

d <- data.frame(
  Name = c("mol1", "mol2"),
  MW = c(300, 400), LogP = c(2, 3), TPSA = c(60, 90),
  "#H-bond acceptors" = c(4, 5), "#H-bond donors" = c(2, 1),
  "#Rotatable bonds" = c(3, 5), check.names = FALSE)
plotRadar(d, id_col = "Name", ids = c("mol1", "mol2"))

TPSA distribution histogram

Description

TPSA distribution histogram

Usage

plotTPSA(data)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(TPSA = c(40, 80, 120))
plotTPSA(d)

t-SNE chemical space plot

Description

Computes a 2-dimensional t-SNE embedding of the selected numeric physicochemical variables and produces a scatterplot, optionally coloured and labelled. Requires the suggested packages Rtsne and ggrepel.

Usage

plotTSNE(
  data,
  variables,
  color_by = "None",
  label_by = "None",
  perplexity = 30,
  max_iter = 1000,
  scale_data = TRUE
)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(
  MW = rnorm(50, 400, 100),
  LogP = rnorm(50, 3, 1.5),
  TPSA = rnorm(50, 80, 40),
  group = rep(c("A", "B"), each = 25)
)
plotTSNE(d, variables = c("MW", "LogP", "TPSA"),
         color_by = "group", perplexity = 5)

Tanimoto / AGNES structural similarity dendrogram

Description

Parses SMILES strings, computes extended fingerprints, builds a Tanimoto similarity matrix and clusters the molecules with AGNES. Requires the suggested packages rcdk, fingerprint and cluster, plus a working Java JDK (for rcdk).

Usage

plotTanimoto(
  data,
  smiles_col,
  label_col = NULL,
  max_n = 40,
  method = "average"
)

Arguments

Value

Invisibly the Tanimoto similarity matrix.

References

Willett, P., Barnard, J. M., & Downs, G. M. (1998). Chemical similarity searching. Journal of Chemical Information and Computer Sciences, 38(6), 983-996.

Examples

d <- data.frame(
  Name = c("ethanol", "aspirin", "benzene", "toluene", "phenol"),
  SMILES = c("CCO", "CC(=O)Oc1ccccc1C(=O)O", "c1ccccc1",
            "Cc1ccccc1", "Oc1ccccc1"),
  stringsAsFactors = FALSE)
plotTanimoto(d, smiles_col = "SMILES", label_col = "Name")

UMAP chemical space plot

Description

Produces a 2D UMAP projection of the selected numeric variables for exploratory analysis of the chemical space. Points can be coloured and labelled. Requires the suggested packages uwot and ggrepel.

Usage

plotUMAP(
  data,
  variables,
  color_by = "None",
  label_by = "None",
  n_neighbors = 15,
  min_dist = 0.1,
  scale_data = TRUE
)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(MW = c(300, 400, 500,600), LogP = c(2, 3, 4,1),
  TPSA = c(60, 80, 100,40), group = c("A", "A", "B", "B"))
plotUMAP(d, variables = c("MW", "LogP", "TPSA"), color_by = "group")

Violations summary bar chart

Description

Produces a stacked bar chart showing, for each drug-likeness rule, the distribution of compounds by number of violations (0, 1, 2, 3+).

Usage

plotViolationsSummary(data)

Arguments

Value

A ggplot2 object.

Violin plot

Description

Produces a violin plot of a numeric variable grouped by a categorical variable, with an optional inner boxplot and jittered points.

Usage

plotViolin(data, variable, group_by, show_box = TRUE, show_points = FALSE)

Arguments

Value

A ggplot2 object.

Examples

d <- data.frame(MW = c(300, 400, 500),
  group = c("A", "A", "B"))
plotViolin(d, variable = "MW", group_by = "group")

Render the ADMETShiny report to HTML, PDF, or Word

Description

Generates a comprehensive report from one or more datasets and renders it to the specified format using rmarkdown. Plots are generated as temporary PNG files and embedded in the document.

Usage

renderReport(datasets, format = "html", output_file)

Arguments

Value

Invisible NULL; called for the side-effect of writing the rendered report to output_file.

Report tab UI

Description

Builds the Report tab of the ADMETShiny application, which generates a comprehensive report of all analyses performed across the four data source modules (CDK & webchem, ADMET Master Manager).

Usage

report_tab()

Value

A shiny.tabPanel.

Launch the ADMETShiny application

Description

Starts the interactive ADMETShiny Shiny application. The function registers the package's static web assets (the hex sticker) and returns a Shiny app object that can be printed or run directly.

Usage

run_app(onStart = NULL, ...)

Arguments

Value

A shiny.appobj object (invisibly); called for the side-effect of launching a Shiny application when printed.

Examples

if (interactive()) {
  admetshiny::run_app()
}

Tutorial tab UI

Description

Builds the Tutorial tab of the ADMETShiny application.

Usage

tutorial_tab()

Value

A shiny.tabPanel.

Veber drug-likeness filter

Description

Filters compounds according to the Veber oral-bioavailability rule (Veber et al., 2002): rotatable bonds and a polarity condition (TPSA or HBA+HBD).

Usage

veberFilter(data, v_rb = 10, v_tpsa = 140, v_hb_sum = 12, violations = 0)

Arguments

Value

A data.frame with the rows of data that satisfy all thresholds.

References

Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615-2623.

Examples

d <- data.frame(TPSA = 80, "#Rotatable bonds" = 3,
  "#H-bond acceptors" = 4, "#H-bond donors" = 2,
  "Veber #violations" = 0, check.names = FALSE)
veberFilter(d)