FLiP-Z by Zimeck: A Python-Based Machine-Learning Tool for Predicting Fungicide-Likeness of Organic Molecules

Development of new agricultural fungicides requires reliable computational tools to screen candidate molecules before investing time, money, and effort in experimental assays. In this study, curated datasets of fungicidal and non-fungicidal compounds were employed in the construction of possible predictive models using diverse machine-learning algorithms and molecular descriptors. The best performance was obtained using balanced datasets and topological descriptors. Two classification models based on different kinds of negative class instances were selected, achieving results of accuracy, sensitivity, and specificity for the test set of 0.806, 0.778, and 0.828 for the first model (RF_BF_14), and 0.937, 0.943, and 0.933 for the second model (RF_BF_17). Differences in performance were consistent with the chemical nature of the negative class. Both models showed excellent applicability domain coverage (>99.6%). Predictions of fungicidal likeness were applied to a curated database of about 1.2 million molecules from the ChEMBL database by using an in-house developed Python3 tool: FLiP-Z (Fungicide Likeness Predictor by Zimeck). Roughly 22% of molecules were predicted by positive consensus as fungicidal candidates. A subsequent screening based on Acute Oral Toxicity reduced the set to 295 molecules with low-toxicity and positive fungicide-likeness predictions. Proprietary rights for FLiP-Z are held by Zimeck C.L.; the tool is available by permission.