Weed control is essential in agriculture. Many strategies have been developed to control these unwanted plants which can bring a lot of damage to the crops. Chemical attack with herbicides (weed killers) is one of the leading directions of struggle with weeds. Taking into account the occurrence of resistance in plants and also the negative impact on the environment of some herbicides, the development of new weedkillers is an urgent problem.

To facilitate a process of finding new herbicides, OTAVAchemicals offers a Herbicides-Like Library comprising of 1,865 compounds. Bayesian statistics and pharmacophore modeling were used for the design of this unique screening collection containing predicted weedkillers.

 See approach details

Known herbicides were clustered into five training sets which have been used for pharmacophore and Bayesian modeling.

Based on FCFP4, FCFP6, ECFP4 and ECFP6 fingerprints and different molecular descriptors (such as LogP, molecular weight, number of rings, number of rotatable bonds, number of hydrogen acceptors and donors and molecular polar surface area) individual Bayesian models were developed for each training set. Then OTAVAchemicals Screening Collection was screened against them. All probabilities of compounds activity were calculated with special mathematical apparatus. Thus Bayesian models estimated how physicochemical parameters, including structural (fingerprints) of the compounds were similar to the template herbicides. Only top-scored compounds were selected.

Also pharmacophore models with different combinations of pharmacophore features and excluded volumes were constructed, optimized and validated using the same training sets. OTAVAchemicals Screening Collection was screened against the best optimized pharmacophore models (from two to six models for each training set). Results were rescored using combination of special rescoring functions, based on pharmacophore feature weights and molecular descriptors (QSAR). The application of the rescoring should allow increasing the number of active compounds identified during screening. Top-scored compounds were selected and visually analyzed. 

The designed library provides an excellent basis for herbicide discovery projects.

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