Summary There are inaccuracies in the chemical families of the WSSA and HRAC herbicide classification systems which could limit their practical use in herbicide‐based weed management strategies. In essence, these inaccuracies could be divided into four parts: (i) the nomenclature of many of the chemical families is not correct, (ii) distinct active ingredients are grouped in same chemical families, (iii) many chemical families have been repeated in at least two modes of action/herbicide groups, and (iv) many active ingredients have not been assigned to chemical family, herbicide group or mode of action. The aim of this study was to revise the current classifications and to propose corrections for the current ones. Detailed investigations on chemical structure of the active ingredients of the registered herbicides showed that some moieties have the same mechanisms of action. According to this study, these moieties have been assigned to the names of chemical families and active ingredients are then classified within the chemical families accordingly. This study has 119 chemical families, compared with 145 in the WSSA system and 58 in the HRAC system. A major priority of this study is the number of active ingredients covered; we included 410 active ingredients with known mechanisms of action and herbicide groups, more than 100 active ingredients more than the current classification systems. Overall, this study provides better opportunities for the management of resistance to herbicides through the application of improved pure and applied knowledge.

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.

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