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

Abstract

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 18,000 researchers from more than 90 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.

A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy

“…The PASS approach [21][22][23][24] was applied in combination with a random forest (RF) classifier to obtain P 1 and P 0 values reflecting the probability that a particular combination was associated with either therapeutic success or failure affecting the particular viral variant. P 1 and P 0 values, calculated by PASS in leave-one-out cross-validation, the number of CD4 + cells, and the number of copies of viral RNA were used as descriptors, as described in the Materials and Methods.…”

Antihypoxic Action of Panax Japonicus, Tribulus Terrestris and Dioscorea Deltoidea Cell Cultures: In Silico and Animal Studies

“…The prediction of PASS is based on the data on more than 1 million structures of compounds and its biological activities. PASS predicts over 7000 types of biological activities (including pharmacotherapeutic effects, mechanisms of action, antitargets, toxic and side effects, change of gene expression, interaction with drug‐metabolizing enzymes and transporters) with accuracy 95 % calculated by leave‐one‐out cross‐validation [12] . PASS prediction result for a structure is displayed as a list of names of biological activities with two values varied from 0 to 1: Pa – probability to be active and Pi – probability to be inactive.…”

Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions

“…Interactions of individual drugs with human proteins were predicted by the PASS Targets software [38]. PASS (Prediction of Activity Spectra for Substances) [41–43] can be used for the prediction of various types of biological activities and is associated with several hundred success stories of its practical application, with experimental confirmation of the prediction results [43, 44]. It uses Multilevel Neighborhoods of Atoms (MNA) descriptors and the Bayesian approach and is available as a desktop program as well as a freely available web service on the Way2Drug platform (http://www.way2drug.com/PASSOnline/) [45].…”