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

2018 ◽  
Vol 1 (1) ◽  
pp. e00004 ◽  
Author(s):  
D.A. Filimonov ◽  
D.S. Druzhilovskiy ◽  
A.A. Lagunin ◽  
T.A. Gloriozova ◽  
A.V. Rudik ◽  
...  

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.

2020 ◽  
Vol 44 (6) ◽  
pp. 2247-2255
Author(s):  
Qifan Zhou ◽  
Lina Jia ◽  
Fangyu Du ◽  
Xiaoyu Dong ◽  
Wanyu Sun ◽  
...  

A novel series of pyrrole-3-carboxamides targeting EZH2 have been designed and synthesized. The structure–activity relationships were summarized by combining with in vitro biological activity assay and docking results.


2005 ◽  
Vol 3 (4) ◽  
pp. 11-18
Author(s):  
Orkhan N Mustafaev ◽  
Serikbay K Abilev ◽  
Viktor A Melnik ◽  
Valentin A Tarasov

Influence of structural features of molecules on antimutagenic activity of flavonoids is investigated. For this purpose the new principle of the description of dependence of biological activity of chemical compounds from their structure is used. It is based on use compound descriptors. It is established, that antimutagenic flavonoids contains C4 keto-group and doubl bond at positions C2 and C3, contains hydroxyl groups. Thus in structure of antimutagenic flavonoids can not be amino-and nitrogroups.


2014 ◽  
Vol 14 (12) ◽  
pp. 963-977 ◽  
Author(s):  
Andrea Milelli ◽  
Carmela Fimognari ◽  
Nicole Ticchi ◽  
Paolo Neviani ◽  
Anna Minarini ◽  
...  

Molecules ◽  
2019 ◽  
Vol 24 (23) ◽  
pp. 4358 ◽  
Author(s):  
Freddy A. Bernal ◽  
Thomas J. Schmidt

Parasitic infections like leishmaniasis and trypanosomiasis remain as a worldwide concern to public health. Improvement of the currently available drug discovery pipelines for those diseases is therefore mandatory. We have recently reported on the antileishmanial and antitrypanosomal activity of a set of cinnamate esters where we identified several compounds with interesting activity against L. donovani and T. brucei rhodesiense. For a better understanding of such compounds’ anti-infective activity, analyses of the underlying structure-activity relationships, especially from a quantitative point of view, would be a prerequisite for rational further development of such compounds. Thus, quantitative structure-activity relationships (QSAR) modeling for the mentioned set of compounds and their antileishmanial and antitrypanosomal activity was performed using a genetic algorithm as main variable selection tool and multiple linear regression as statistical analysis. Changes in the composition of the training/test sets were evaluated (two randomly selected and one by Kennard-Stone algorithm). The effect of the size of the models (number of descriptors) was also investigated. The quality of all resulting models was assessed by a variety of validation parameters. The models were ranked by newly introduced scoring functions accounting for the fulfillment of each of the validation criteria evaluated. The test sets were effectively within the applicability domain of the best models, which demonstrated high robustness. Detailed analysis of the molecular descriptors involved in those models revealed strong dependence of activity on the number and type of polar atoms, which affect the hydrophobic/hydrophilic properties causing a prominent influence on the investigated biological activities.


Sign in / Sign up

Export Citation Format

Share Document