Nowadays the development of information technologies based on bioinspired intellectual approaches for the computer design of new drugs and forecasting of their properties is an urgent task. The research is devoted to the development of an intellectual information system for conducting scientific researches and forecasting the structure-property/activity relationship of new drugs based on artificial immune systems approach. In accordance with the concept of multi-algorithmic approach, the construction of an optimal immune network model and the allocation of informative descripts are carried out using swarm intelligence algorithms: modified algorithms of ant colonies and particle swarms. The developed information system allows selecting the best algorithm for preliminary data processing, in which after immune network modeling, the value of generalization error will be minimal. The use of multi-algorithm approach at immune network modeling of drugs requires the systematization of used algorithms and the creation of an integrated ontological model, which allows structuring the input and outputting data. There is presented an example of the database of sulfanilamides with different pharmacological activity, also modeling results and comparative analysis of the use of various algorithms of swarm intelligence.
Nowadays modern methods of artificial intelligence are widely used in pharmacology
for computer modeling of new drug compounds with pre-defined properties. The
creation and investigation of chemical compounds is associated with the processing of
multidimensional data. The development of information technologies based on
intellectual approaches for processing and analysis a large data sets and solution of
forecasting is an actual problem. Neural networks [
In: S. Belim et al. (eds.): OPTA-SCL 2018, Omsk, Russia, published at http://ceur-ws.org
least square regression method for breast cancer diagnosis. The proposed approach
has a significant effect on the classification accuracy for clinical diagnosis and can be
used to solve detection problems. The research [
Forecasting quantitative structure-activity relationship (QSAR) of drugs and identifying the links between compounds structure and their activity is an actual problem in pharmacology. One of the important steps in the process of forecasting structureproperty/activity relationship is the selection of informative descriptors for reducing the size of a descriptor space.
Nowadays bioinspired intellectual approaches to solve optimization problems are
actively developed. Swarm intelligence algorithms, such as ant colony optimization
(ACO) and particle swarm optimization (PSO), which based on animal and insect life
behavior to find the shortest path between food source and their nests, are applied in
QSAR modeling [
At construction the optimal immune network model for forecasting QSAR of
drugs, there is relevant to use an ontological approach, which allows structuring the
input and output data, take into account the features of functioning and
interconnection, and save the time and computing resources while developing the information
system. In paper [
The following structure of the article is proposed: Section 2 describes problem statement of the research and the immune network technology for forecasting of QSAR of chemical compounds. Section 3 presents the creation of ontological models of swarm intelligence algorithms. Section 4 is devoted to the development an information system of forecasting for conducting scientific researches “SIIM” (Swarm intelligence for immune network modeling) and the description of the database of sulfonamides with different duration of action. Section 5 presents the modeling results using the chemical compounds of sulfanilamide group as an example and comparative analysis of used algorithms. At the end of the article, conclusion and references are presented. 2
The problem statement is formulated as follows: it is necessary to develop an information system of forecasting for conducting scientific researches ”SIIM” (Swarm intelligence for immune network modeling) for creation an optimal immune network model of drug compounds of sulfanilamide group based on swarm intelligence algorithms: modified algorithms of ant colonies and particle swarms.
Definition: optimal immune network is a network constructed based on the weight
coefficients of the selected informative descriptors and most fully characterizing the
considered chemical compound. The criterion of optimization is the storage of
maximum information at a minimum number of descriptors [
The intellectual technology for forecasting the properties of new chemical
compounds consists of the stages of preliminary data processing, immune network
training, image recognition, energy error estimation and selection of candidates of
drug compounds [
The development of integrated ontological model (OM) allows to study of subject
domain of AIS in detail and to analyze of swarm intelligence algorithms deeply. The
use of modern ontological editors and the creation of OM facilitate the solution of
problem of selection informative descriptors and the construction of an optimal
immune network model. As a tool for developing OM, there is chosen the ontology
editor Protégé [
The integrated OM of immune network technology, which consists of OM of preliminary data processing, OM of image recognition and OM of energy error estimation of AIS has been proposed. The integrated OM is presented in the form of a tuple of sets: ОМINT = <OMPR, OMIR, OMEEE>, where OMPR – OM of preliminary data processing;
OMIR – OM of image recognition based on AIS;
OMEEE – OM of energy error estimation of AIS.
The ontological model of preliminary data processing consists of ontological models of algorithms of ant colony and particle swarms:
ОМPR = <OMACO, OMPSO>, where OMACO – OM of algorithms of ant colony;
OMPSO – OM of algorithms of particle swarms.
There are many modifications of classical swarm intelligence algorithms for preliminary data processing. Ant colony algorithm has several modifications such as AntSrank, Max-min ant system, Elitist ant system, etc. In addition, particle swarm algorithm has following modifications: CoPSO, Fully informed PSO, Inertia Weighted PSO, etc. Table 1 shows OM of swarm intelligence algorithms, OM of image recognition, OM of energy error estimation.
Ontological model Ontological model of algorithms of ant colony
Swarm intelligence algorithms at selection of informative descriptors show different
results depend on the size and quality of data, the availability of independent
parameters and the optimality criteria. There are no universal algorithms for preliminary data
processing. The advantage of using a multi-algorithmic approach is the possibility of
choosing swarm intelligence algorithm, which allows to create an immune network
model with the best prognostic properties and shows the minimum value of
generalization error of AIS. Image recognition and energy error the estimation of AIS are
described in work [
Information system of forecasting for conducting scientific researches «SIIM» [
As the database has been used a database of descriptors of sulfanilamide group with pre-defined pharmacological properties on the basis of the resource Mol-instincs and PubChem [25]. The database consists more than 1500 descriptors. Table 2 shows a fragment of the sulfanilamide database, which are classified into short acting, medium acting and long acting sulfanilamides.
At modeling sulfanilamides based on PSO algorithm the population size is 100, iteration number is 50, с1 (weight) = 1, с2 (velocity) = 2, report frequency equal to 50. As a result, there were selected 49 informative descriptors from 1500 ones. Figure 1 shows the graph of selected informative descriptors based on PSO algorithm. If at modeling the population size is 200, then, as a result, there are selected 11 informative descriptors from 1500 ones (Fig. 2). From comparison purposes, both algorithms have the same iteration numbers and population sizes. The comparative analysis allows to define optimization algorithm with the best performance and low execution time. Table 3 shows a comparison of modeling results based on algorithms of ant colonies and particle swarms. The amount of population size and iterations affect the effectiveness of swarm intelligence algorithms. A large number of populations and iterations allows agents to explore descriptor space more detailed and reduce the number of selected informative descriptors. The running time of PSO algorithm is 8 seconds when the population size is equal to 100 particles. The running time of ACO algorithm is 20 seconds with population size of 100. The modeling results show, PSO algorithm is considered as best optimization method with minimum execution time. 6
Therefore, the study of structure-property/activity relationship of drug compounds,
the development of new non-traditional intellectual approaches of QSAR and the
computer molecular design of drugs with pre-defined properties are one of the most
actual and main tasks of modern pharmacology aimed at reduction the time and cost
of creating new drugs. The developed information system for conducting scientific
researches “SIIM” with the use of immune network technology for forecasting
structure-property/activity relationship of chemical compounds and multi-algorithmic
approach, which allows integrating different methods of artificial intelligence to solve
the problem of computer molecular design of new drugs with pre-defined properties.
The application of multi-algorithmic approach with the use of modified algorithms of
ant colonies and particle swarms allows performing preliminary data processing
efficiently, allocating the informative set of descriptors and creating an optimal immune
network model. At developing an information system, the use of ontological models
allows structuring data and analyzing the hidden interactions between descriptors. The
advantage of immune network modeling technology is the energy error estimation by
homologues [
The work was carried out according to the grant of the CS MES RK on the theme: "Development and analysis of databases for the information system for prediction the "structure-property" dependence of drug compounds based on artificial intelligence algorithms" (2018-2020).