The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by computational target prediction tools (CTPTs). However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. A minimum structure is identified for each group of neighbor molecules in the proposed method. Each group of neighbor molecules represents a distinct structural class of molecules with the same function in relation to the target. The minimum structure is employed as a query to search for molecules that perfectly satisfy the minimum structure of what is guessed crucial for the targeted activity. The proposed method is based on chemical similarity, but only molecules that perfectly satisfy the minimum structure are considered. Structurally related bioactive molecules found with the same minimum structure were considered as neighbor molecules of the query molecule. The known target of the neighbor molecule is used as a reference for predicting the target of the neighbor molecule with an unknown target. A lot of information is needed to identify the minimum structure, because it is necessary to know which part(s) of the bioactive molecule determines the precise target or targets responsible for the observed phenotype. Therefore, the predicted target based on the minimum structure without employing the statistical significance is considered as a reliable prediction. Since only molecules that perfectly (and not partly) satisfy the minimum structure are considered, the minimum structure can be used without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred, and SPiDER), which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB, and PASS online, showed the best quality and quantity for the accurate predictions.

We revealed critical vulnerabilities of nutrient content claims under FDA per serving size (serving), CAC per 100 g or mL, CAC per serving, and CAC per 100 kcal and developed the proposed method to remove all vulnerabilities. We calculated the nutrient content of foods based on nutrient content claims under the proposed method. Then, we determined nutrient content claims for foods and food groups, and specified similarities between nutrient content claims in food groups. Also, we ranked foods and food groups based on met claims of the nutrient content. Nutrient content claims based on the proposed method, including free, very low, low, source, and high claims were determined in 8596 food items, 29 nutrients, and 25 food groups of the USDA National Nutrient Database for Standard Reference, release 28. Source and high claims are used for positive nutrients (including vitamins, minerals except sodium, protein, and dietary fiber). The very low claim is used for sodium, and free and low claims are used for cholesterol, energy, saturated fat, sodium, and total fat (also known as negative nutrients). In general, critical vulnerabilities of nutrient content claims under FDA and CAC can cause: (1) excessive energy intake based on FDA and CAC per serving and CAC per 100 g or mL; (2) exceeding the DV (NRV) for low nutrients under FDA per serving and CAC per 100 g or mL by consuming small amounts of foods per day; (3) the presence of nutrient free, but not low nutrient, foods based on FDA per serving and CAC per 100 g or mL; (4) the calculation of nutrient content in inappropriate amounts of foods based on FDA per serving, CAC per 100 g or mL, CAC per 100 kcal, and CAC per serving; and (5) determination of energy from total fat and saturated fat for relevant claims in inappropriate portions based on FDA per serving and CAC per 100 g or mL. Breakfast cereals, baby foods, pork products, lamb, veal, and game products, poultry products, and beef products had the highest average of scores for source and high nutrients. Restaurant foods, fast foods, and sausages and luncheon meats had the lowest average of scores for free, very low, and low nutrients. Nutrient source or high nutrient foods for all 24 positive nutrients were found in seven food groups (American Indian/Alaska Native foods; baby foods; beverages; dairy and egg products; legumes and legume products; meals, entrees, and side dishes; vegetables and vegetable products). There were very few source and high foods for potassium and vitamin D. Also, there were very few energy free foods.

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