Biomedical Chemistry: Research and Methods, 2018, 1(1), e00004

Computer-aided prediction of biological activity spectra
for chemical compounds: opportunities and limitations

D.A. Filimonov 1, D.S. Druzhilovskiy 1, A.A. Lagunin 1,2, T.A. Gloriozova 1, A.V. Rudik 1, A.V. Dmitriev 1, P.V. Pogodin 1, V.V. Poroikov 1*

1Institute of Biomedical Chemistry, 8119121 Pogodinskaya Str., 10 bldg. 8, Moscow, Russia,*e-mail: vladimir.poroikov@ibmc.msk.ru
2Pirogov Russian National Research Medical University, 117997, Ostrovityanova Str., 1, Moscow, Russia.

Keywords: analysis of structure-activity relationships, biological activity spectra, computer-aided prediction, PASS, accuracy, predictivity, web-resource Way2Drug

DOI: 10.18097/BMCRM00004

The whole version of this paper is available in Russian.

An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes.

The web resource Way2Drug is used by over 19 000 researchers from more than 100 countries around the world, which allowed them to obtain over 600 000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra.

Figure 1. A cloudy representation of all biological activities predicted by PASS (version 2017). The font size is proportional to the number of compounds with the appropriate activity in the training set.
Figure 2. MNA descriptors for the sulfur atom in the Topiramate molecule (PubChem CID: 5284627). The atoms of the fragment covered by MNA/2 descriptor is highlighted in blue.
Figure 3. The structural formula of the Topiramate molecule and the set of MNA descriptors describing the structure of this molecule in PASS.
Figure 4. Dependencies of Pa (B) and Pi (B) for "Antihypertensive" activity estimated using the data presented in SAR Base PASS version 2017.
Figure 5. An example of the relationships between the Sensitivity (Sensitivity ≡ 1-Pa, black curve), Specificity (Specificity ≡ 1-Pi, red line), Concordance (blue curv), and Balanced Accuracy (BA = (Sensitivity + Specificity) / 2) as a function of the threshold for the probability of errors of the second kind (Pi) for the activity "Antitumor"
Figure 6. An example of selecting threshold values Pa based on the prediction of antihypertensive activity for the reference drug Moexipril Hydrochloride.
Figure 7. Restricting the prediction of biological activity spectrum only by the list of mechanisms and effects associated with antihypertensive action.

CLOSE
Table. Predicted by PASS online biological activity spectrum Topiramate molecule (PubChem CID: 5284627) at the threshold

ACKNOWLEDGEMENTS

The work was supported in the framework of the Russian State Academies of Sciences Fundamental Research Program for 2013-2020. The authors express sincere gratitude to the Clarivate analytics for the license on Integrity, and ChemAxon for the license on JChem.

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