Many people simultaneously exhibit multiple diseases, which complicates efficient medical treatments. For example, patients with cancer are frequently susceptible to infections. However, developing drugs that could simultaneously target several diseases is challenging. We present a novel theoretical method to assist in selecting compounds with multiple therapeutic targets. The idea is to find correlations between the physical and chemical properties of drug molecules and their abilities to work against multiple targets. As a first step, we investigated potential drugs against cancer and viral infections. Specifically, we investigated antimicrobial peptides (AMPs), which are short positively charged biomolecules produced by living systems as a part of their immune defense. AMPs show anticancer and antiviral activity. We use chemoinformatics and correlation analysis as a part of the machine-learning method to identify the specific properties that distinguish AMPs with dual anticancer and antiviral activities. Physical-chemical arguments to explain these observations are presented.