The spread of antimicrobial resistance has become a serious public health concern, making once-treatable diseases deadly again and undermining the achievements of modern medicine. Drug combinations can help to fight multi-drug-resistant bacterial infections, yet they are largely unexplored and rarely used in clinics. Here we profile almost 3,000 dose-resolved combinations of antibiotics, human-targeted drugs and food additives in six strains from three Gram-negative pathogens-Escherichia coli, Salmonella enterica serovar Typhimurium and Pseudomonas aeruginosa-to identify general principles for antibacterial drug combinations and understand their potential. Despite the phylogenetic relatedness of the three species, more than 70% of the drug-drug interactions that we detected are species-specific and 20% display strain specificity, revealing a large potential for narrow-spectrum therapies. Overall, antagonisms are more common than synergies and occur almost exclusively between drugs that target different cellular processes, whereas synergies are more conserved and are enriched in drugs that target the same process. We provide mechanistic insights into this dichotomy and further dissect the interactions of the food additive vanillin. Finally, we demonstrate that several synergies are effective against multi-drug-resistant clinical isolates in vitro and during infections of the larvae of the greater wax moth Galleria mellonella, with one reverting resistance to the last-resort antibiotic colistin.

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Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair’s activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.

Natural products constitute a vast yet largely untapped resource of molecules with therapeutic properties. Computational approaches based on structural similarity offer a scalable approach for evaluating their bioactivity potential. However, this remains challenging due to the immense structural diversity of natural compounds and the complexity of structure-activity relationships. We here assess the bioactivity potential of natural compounds using random forest models utilizing structural fingerprints, maximum common substructure, and molecular descriptors. The models are trained with small-molecule drugs for which the corresponding protein targets are known (1,410 drugs, 0.9 million pairs). Using these models, we evaluated circa 11k natural compounds for functional similarity with therapeutic drugs (1.7 million pairs). The resulting natural compound-drug similarity network consists of several links with support from the published literature as well as links suggestive of unexplored bioactivity of natural compounds. As a proof of concept, we experimentally validated the model-predicted Cox-1 inhibitory activity of 5-methoxysalicylic acid, a compound commonly found in tea, herbs and spices. In contrast, a control compound, with the highest similarity score when using the most weighted fingerprint metric, did not inhibit Cox-1. Our results illustrate the importance of complementing structural similarity with the prior data on molecular interactions, and presents a resource for exploring the therapeutic potential of natural compounds.