A Novel Prediction Model for Discovering Beneficial Effects of Natural Compounds in Drug Repurposing

Natural compounds are promising leads in drug discovery due to their low toxicity and synergistic effects existing in nature, providing efficient and low-cost therapeutic solutions. Synergistic effects are observed in highly similar or closely related compounds where the combined effect is much more significant than individual usage. However, multiple hurdles exist in the identification of similar compounds, in particular, accumulation of large volumes of compounds, procurement of authentic information, diversity and complexity of the compounds, convoluted mechanism of action, need of high-throughput screening and validation techniques, most importantly incompleteness of critical information like indications for the natural compounds. Currently, not many comprehensive computational pipelines are available for drug discovery using natural products. To overcome these challenges, in this study, we focus on predicting highly similar candidate compounds with synergistic effects useful in combinatorial/alternative therapies. We developed a molecular compound similarity prediction model for computing four different compound-compound similarity scores based on (i) bioactivity, (ii) chemical structure, (iii) target enzyme, and (iv) protein functional domain, using the data from public repositories. The calculated scores are combined efficiently for predicting highly similar compound pairs with similar biological or physicochemical properties. We evaluate the accuracy of our model with pharmacological and bioassay results, and manually curated literature from PubChem, NCBI, etc. As a use case, we selected 415 compounds based on 13 functional categories, out of which 66 natural compounds with 198 compound-compound similarity scores were identified as top candidates based on similar bioactivities, chemical substructures, targets, and protein functional sites. Statistical analysis of the scores revealed a significant difference in the mean similarity scores for all four categories. Twenty-eight closely interacting compounds, including Quercetin, Apigenin, etc. were identified as candidates for combinational therapies showing synergistic effects. Herbs, including Dill, Basil, Garlic, Mint, etc., were predicted as potential combinations for achieving synergistic effects. Twenty-four compounds with unknown pharmacological effects were associated with 58 potential new pharmacological effects/indications. If applied broadly, this model can address many problems in chemogenomics and help in identifying novel drug targets and indications, which is a critical step in natural drug discovery research and evidence for drug-repurposing.