DECISION MAKING IN ANTI-CORONAVIRUS DRUG DISCOVERY: MATHEMATICAL MODELING AND VALUE OF INFORMATION ANALYSIS

Background. The process of preclinical evaluation of antiviral medications often consists of multiple stages, each containing substantial uncertainties. Traditional methods for screening the compounds often lack structured means for optimizing the decision-making and calculating the feasibility and risks of transitions between all of the stages. Thus, there appears to be a problem with the inefficient selection of promising antiviral molecules, which subsequently increases the probability of choosing suboptimal research trajectories. Objective. To develop a computational framework for optimizing of the transition between stages in preclinical antiviral testing. The system focuses on the integration of decision trees and Markov models in order to include effectiveness, risks and the value of additional information into assessment, supporting an in-depth planning of preclinical research pipelines. Methods. Experimental data from molecular docking, cytotoxicity CD50, and antiviral activity IC50 were used in a multi-stage evaluation system with CTI ≥ 4 being the criterion for progression into further stages. Decision trees provided the explicit rules for advancement of the compounds, while Markov models added context for building sequential strategies under uncertainty and quantified the feasibility of movement to the next stage. Value of information analysis added the assessment of the expected benefit of additional data. Results. The developed framework consistently produced reliable technical results. The decision used in CTI ≥ 4.0 prediction stage demonstrated a conservative classification pattern, correctly identifying compounds with high therapeutic potential while missing some effective candidates. The Markov model showed steadily increasing state values in docking, cytotoxicity, and antiviral testing phases that confirmed the growth of expected utility. Value of information analysis highlighted that the largest gain occurred after antiviral activity testing. Conclusions. The study showed that both decision trees and Markov models capture different but complementary aspects of the preclinical evaluation process . Decision trees provide interpretable set of rules that formalize how molecular docking and cytotoxicity measurement influence the progression of compounds, while their limited sensitivity at the CTI threshold highlighted the complexity of prediction the final success of the evaluated compounds. The Markov model simulations showed that the full three-stage pipeline is justified and that progression decisions are influenced by both uncertainty and experimental cost. The value of information analysis clarify the importance of each stage, helping to emphasize the role of antiviral activity data in reducing uncertainty. These findings support the integration of analytic methods for improving the structure, transparency and efficiency of antiviral preclinical research.