The acronym PASS stands for Prediction of Activity Spectra for Substances. Upon entering a structural formula of a chemical substance, the program returns the potential biological activities of this compound.
PASS has been well accepted by the community, and is now actively used in the field of medicinal chemistry, by both academic organizations and pharma companies.
There are over 200 third-party publications with references to PASS. Some of the most recent papers that provide experimental evaluation of PASS predictions are listed

Application areas
  • Medicinal chemistry
  • Computational chemistry
  • Drug discovery / drug development
  • Drug repositioning
  • Chemical toxicity
  • Safety assessment
  • Pharmacogenomics, chemogenomics
  • SAR (qualitative structure-activity relationship)
  • Natural compound effects
  • Translational research / translational medicine

Activity prediction for a chemical substance by PASS

The slide show demonstrating the look-and-feel of PASS can be found here as well as on our Facebook site. A particularly useful tool to analyze and utilize PASS results further is PharmaExpert.

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What PASS can do

The input compound can be submitted in MOLfile or SDfile format. The output contains the following predicted activities:
General overview of all biological activities assigned to the input compound (out of a list of 7527 items)
Pharmacotherapeutic effects (464; example: antimetastatic effect)
Biochemical mechanisms (3850; example: xanthine oxidase inhibitor)
Toxicity, i.e. adverse and toxic effects (321; example: arrhythmogenic)
Metabolism (195; example: CYP2D6 substrate)
Gene regulation expression (1610; example: VEGF expression inhibition)
Transporter-related activities (68; example: sodium/calcium exchanger inhibitor)

Free demo

Thank you very much for your interest in the programs PASS and PharmaExpert!

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PASS packages

  • PASS
  • Standard software package, which includes the standard SAR base (Structure-Activity Relationship base). Current standard version of PASS can predict over 4.300 different biological activities.
  • PASS Pro
  • PASS Professional package provides all functions of PASS and the additional option to create, train and validate your proprietary SAR base. With this package, you could make your own and unique SAR base, and use it further for predictions on other compounds. Your own SAR base can be used as it is, or can be combined with the standard SAR base. Thus, locally you would have a unique variant of PASS.
  • PASS Light
  • With PASS Light, you can create, train and validate your proprietary SAR base, and use it for further predictions. The standard SAR base is not included.
  • PASS customized
  • According to your potential focus on particular types of biological activities, a customized variant of PASS can be made to predict a restricted number of activities as per your selection.
  • PASS product + PharmaExpert
  • Any of the products PASS, PASS Pro, PASS Light and PASS customized, can be ordered in a package with PharmaExpert.

Last release features

  • Improved structure-activity relationship (SAR) Base contains 313,345 substances and 75,875 descriptors.
  • Number of predictable activity types has increased by more than 3000 terms, and number of recommended activity types has increased by more than 2000 terms.
  • The number of predictable effects on gene expression has considerably been increased by more than 1500 terms.
  • The number of predictable mechanisms of action has been increased by more than 470 terms.
  • New functionality has been added, enabling now direct communication between PASS and structure-drawing programs, e.g. MarvinSketch
    – Copy any individual structure from PASS directly to clipboard, as Bitmap or as MOL file
    – Open a structure in PASS directly from clipboard
  • See the pass release announcement for further details.


The basis > How it works

To execute the prediction, PASS requires a knowledge base about structure-activity relationships for compounds with known biological activities. This is provided by SAR Base, containing the analysis results obtained with an in-house training set of more than 250,000 compounds with known biological activities. This training set is continuously curated and expanded. SAR Base can also be replaced by an exclusive knowledge base, which can be created using in-house data. The knowledge base together with the user-defined constraints of biological activities of interest and relevant parameters provides PASS the starting point for the computational prediction.

PASS applications

  • Medicinal chemistry
  • Computational chemistry
  • Drug discovery / drug development
  • Drug repositioning
  • Chemical toxicity
  • Safety assessment
  • Pharmacogenomics, chemogenomics
  • SAR (qualitative structure-activity relationship)
  • Natural compound effects
  • Translational research / translational medicine

Information downloads


Ivanov, S.M., Lagunin, A.A., Zakharov, A.V., Filimonov, D.A., Poroikov, V.V. (2013) Computer Search for Molecular Mechanisms of Ulcerogenic Action of Non-Steroidal Anti-Inflammatory Drugs. Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry 7:40–45. link.

Korolev, S.P., Kondrashina, O.V., Druzhilovsky, D.S., Starosotnikov, A.M., Dutov, M.D., Bastrakov, M.A., Dalinger, I.L., Filimonov, D.A., Shevelev, S.A., Poroikov, V.V., Agapkina, Y.Y., Gottikh, M.B. (2013) Structural-Functional Analysis of 2,1,3-Benzoxadiazoles and Their N-oxides As HIV-1 Integrase Inhibitors. Acta Naturae 5:63–72. 23556131.

Lagunin, A.A., Gloriozova, T.A., Dmitriev, A.V., Volgina, N.E., Poroikov, V.V. (2013) Computer evaluation of drug interactions with P-glycoprotein. Bull. Exp. Biol. Med. 154:521–524. 23486596.

Zakharov, A.V., Peach, M.L., Sitzmann, M., Filippov, I.V., McCartney, H.J., Smith, L.H., Pugliese, A., Nicklaus, M.C. (2012) Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med. Chem. 4:1933–1944. 23088274.

Filz, O.A., Lagunin A.A., Filimonov D.A., Poroikov V.V. (2012) In silico fragment-based drug design using a PASS approach. SAR QSAR Environ. Res. 23:279–296. PubMed.

Lagunin A., Zakharov A., Filimonov D., Poroikov V. (2011) QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol. Inform. 30:241–250. Link.

Lagunin A., Filimonov D., Poroikov V. (2010) Multi-targeted natural products evaluation based on biological activity prediction with PASS. Curr. Pharm. Des. 16:1703-1717. PubMed.

Geronikaki A., Vicini P., Dabarakis N., Lagunin A., Poroikov V., Dearden J., Modarresi H., Hewitt M., Theophilidis G. (2009) Evaluation of the local anaesthetic activity of 3-aminobenzo[d]isothiazole derivatives using the rat sciatic nerve model. Eur. J. Med. Chem. 44:473-481. PubMed.

Geronikaki A.A., Lagunin A.A., Hadjipavlou-Litina D.I., Eleftheriou P.T., Filimonov D.A., Poroikov V.V., Alam I., Saxena A.K. (2008) Computer-aided discovery of anti-inflammatory thiazolidinones with dual cyclooxygenase/lipoxygenase inhibition. J. Med. Chem. 51:1601-1609. PubMed.

Geronikaki A., Druzhilovsky D., Zakharov A., Poroikov V. (2008). Computer-aided predictions for medicinal chemistry via Internet. SAR & QSAR Environ. Res. 19:27-38. PubMed

Sergeiko A., Poroikov V.V., Hanus L.O., Dembitsky V.M. (2008) Cyclobutane-containing alkaloids: origin, synthesis, and biological activities. Open Med. Chem. J. 15:26-37. PubMed.

Devillers J., Marchand-Geneste N., Doré J.C., Porcher J.M., Poroikov V. (2007) Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools. SARQSAR Environ. Res. 18:181-193. PubMed.

Poroikov V., Filimonov D., Lagunin A., Gloriozova T., Zakharov A. (2007) PASS: identification of probable targets and mechanisms of toxicity. SAR QSAR Environ. Res. 18:101-110. PubMed.

Devillers J., Doré J.C., Guyot M., Poroikov V., Gloriozova T., Lagunin A., Filimonov D. (2007) Prediction of biological activity profiles of cyanobacterial secondary metabolites. SAR QSAR Environ. Res. 18:629-643. PubMed.

Dembitsky V.M., Gloriozova T.A., Poroikov V.V. (2007) Natural peroxy anticancer agents. Mini Rev. Med. Chem. 7:571-589. PubMed.

Lagunin A.A., Zakharov A.V., Filimonov D.A., Poroikov V.V. (2007) A new approach to QSAR modelling of acute toxicity. SAR QSAR Environ. Res. 18:285-298. PubMed.

Click for a more extended PASS bibliography.