Computational Evaluation of ADME Parameters: A SwissADME Tutorial

In the drug development process, pharmacokinetic properties are as critical as the biological activity of a molecule. Understanding how a potential drug candidate behaves in the body—whether it can reach the target site at sufficient concentrations and how long it remains in its bioactive form—constitutes one of the cornerstones of successful drug development. ADMET analysis provides a systematic framework for these evaluations, and in silico models offer valid alternatives, particularly at early stages when access to physical samples is limited (1).

The ADMET Concept and Its Importance

The term ADMET refers to five fundamental processes that a drug undergoes in the body: Absorption, Distribution, Metabolism (Biotransformation), Excretion, and Toxicity. These parameters play a decisive role in determining whether a molecule can be developed into a drug. Absorption describes how efficiently a drug enters systemic circulation; distribution refers to how it spreads across different tissues; metabolism defines the biochemical transformations it undergoes in the body; excretion describes how it is eliminated; and toxicity refers to its potential harmful effects (2). In modern drug discovery, approximately 40% of drug candidates fail due to inadequate ADME properties. Therefore, evaluating these characteristics at an early stage is crucial in terms of both time and cost efficiency (3).

SwissADME: An in silico Tool for ADME Evaluation

SwissADME is a free web-based server developed by the Swiss Institute of Bioinformatics and has been available since 2017. It provides fast and reliable predictive models for estimating the physicochemical properties, pharmacokinetic parameters, drug-likeness, and medicinal chemistry friendliness of small molecules. One of the major strengths of SwissADME is that it does not require user registration and, благодаря its user-friendly interface, it can be easily used by both experts and researchers with little or no experience in cheminformatics (1). The SwissADME tool incorporates proprietary methods such as BOILED-Egg, iLOGP, and the Bioavailability Radar. These approaches facilitate rapid decision-making by visualizing gastrointestinal absorption, blood–brain barrier permeability, and oral bioavailability potential of molecules (4).

Step-by-Step ADME Analysis with SwissADME

Step 1: The SwissADME web server can be accessed free of charge at http://www.swissadme.ch/. On the homepage, two different input options are available. On the left side, molecules can be drawn using the MarvinJS editor, or alternatively, molecular codes can be entered in SMILES (Simplified Molecular Input Line Entry Specification) format on the right side. As an example application, the aspirin molecule will be considered.

Figure 1. Homepage of the SwissADME Tool.

The SMILES format is a simple notation that represents chemical structures as a single line of text. In the example application, the SMILES code “CC(=O)Oc1ccccc1C(=O)O” can be used for the aspirin (acetylsalicylic acid) molecule. If you wish to analyze multiple molecules, you can enter the SMILES codes line by line, with one molecule per line, and optionally add the molecule name after a space.

Step 2: After entering the SMILES format for aspirin, you can start the analysis by clicking the “Run!” button. The computation time may vary from a few seconds to several minutes, depending on the number of molecules submitted.

Figure 2. Query Interface of the SwissADME Tool.

Step 3: Once the analysis is complete, SwissADME presents the outputs in several distinct sections, including:

Physicochemical Properties: This section includes fundamental parameters such as the molecular formula, molecular weight, number of hydrogen bond donors and acceptors, number of rotatable bonds, topological polar surface area (TPSA), and lipophilicity (LogP).

Lipophilicity: Lipophilicity reflects a molecule’s tendency to dissolve in lipids and is crucial for membrane permeability. SwissADME calculates LogP values using five different methods. For ideal drug-like molecules, LogP values generally fall within the range of 0–5 (5).

Water Solubility: This parameter estimates how soluble the molecule is in water. Adequate aqueous solubility is essential for good absorption.

Pharmacokinetic Properties: Predictions are provided for gastrointestinal (GI) absorption, blood–brain barrier (BBB) permeability, whether the molecule is a P-glycoprotein substrate, and potential inhibition of cytochrome P450 enzymes. High GI absorption indicates that the drug is likely to be well absorbed following oral administration.

Drug-Likeness Filters: Various criteria are evaluated, including Lipinski’s Rule of Five, Ghose, Veber, Egan, Muegge, and PAINS filters. According to Lipinski’s rule, a good oral drug candidate should have a molecular weight ≤500 Da, LogP ≤5, no more than 5 hydrogen bond donors, and no more than 10 hydrogen bond acceptors (6).

Synthetic Accessibility: This metric estimates the difficulty of synthesizing the molecule on a scale from 1 to 10. Values closer to 1 indicate molecules that are easy to synthesize, whereas values closer to 10 represent highly complex structures.

Şekil 3. SwissAdme Tarafından Aspirin için Elde Edilen Özellikler.

When the aspirin molecule is analyzed using the SwissADME tool, the “Physicochemical Properties” and “Lipophilicity” sections indicate a molecular weight of 180.16 Da, a LogP value of approximately 1.19, one hydrogen bond donor, and four hydrogen bond acceptors. Examination of the “Druglikeness” and “Medicinal Chemistry” sections shows that aspirin successfully passes the Lipinski, Veber, Ghose, Egan, and PAINS filters. In addition, a synthetic accessibility value close to 1 suggests that aspirin is an easily synthesizable molecule. The parameters listed under “Pharmacokinetics” demonstrate high gastrointestinal absorption, indicating good oral bioavailability. Furthermore, aspirin is predicted to be capable of crossing the blood–brain barrier. The fact that it does not inhibit any CYP enzymes is also a favorable finding in terms of reduced risk for drug–drug interactions (7).

Step 4: All obtained results can be downloaded in CSV format from the “Retrieve data” section and opened in Excel for more detailed analyses. When interpreting the results, it is important to consider all parameters collectively rather than focusing on a single metric.

Advantages and Limitations of SwissADME

Advantages:

• Free and user-friendly interface

• No registration required

• Rapid generation of results

• Comprehensive range of parameters

• Capability for batch analysis

• Visual outputs (BOILED-Egg, Bioavailability Radar)

Limitations:

• Predictions require experimental validation

• Inability to fully model complex biological interactions

• Limited scope of toxicity predictions (8)

SwissADME is a powerful in silico tool for early-stage screening in the drug discovery process. By enabling rapid evaluation of pharmacokinetic properties and drug-likeness, it helps researchers decide which compounds merit more detailed investigation. Understanding the ADMET profiles of potential drug candidates prior to experimental studies provides significant savings in both time and resources. However, it should always be kept in mind that in silico predictions must be validated with experimental data. Tools such as SwissADME have become an integral part of modern drug development workflows, serving as essential aids for rational drug design.

References

1. Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1), 42717. https://doi.org/10.1038/srep42717 

2. van de Waterbeemd, H., & Gifford, E. (2003). ADMET in silico modelling: towards prediction paradise? Nature Reviews Drug Discovery, 2(3), 192-204. https://doi.org/10.1038/nrd1032 

3. Kola, I., & Landis, J. (2004). Can the pharmaceutical industry reduce attrition rates? Nature Reviews Drug Discovery, 3(8), 711-716. https://doi.org/10.1038/nrd1470 

4. Daina, A., & Zoete, V. (2016). A BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 11(11), 1117-1121. https://doi.org/10.1002/cmdc.201600182 

5. Arnott, J. A., & Planey, S. L. (2012). The influence of lipophilicity in drug discovery and design. Expert Opinion on Drug Discovery, 7(10), 863-875. https://doi.org/10.1517/17460441.2012.714363 

6. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 46(1-3), 3-26. https://doi.org/10.1016/s0169-409x(00)00129-0 

7. Vane, J. R., & Botting, R. M. (2003). The mechanism of action of aspirin. Thrombosis Research, 110(5-6), 255-258. https://doi.org/10.1016/s0049-3848(03)00379-7 

8. Lagorce, D., Douguet, D., Miteva, M. A., & Villoutreix, B. O. (2017). Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors. Scientific Reports, 7(1), 46277. https://doi.org/10.1038/srep46277