The development of new molecules for the treatment of calmodulin related cardiovascular or neurodegenerative diseases is an interesting goal. In this work, we introduce a novel strategy with four main steps: (1) chemical synthesis of target molecules, (2) Förster Resonance Energy Transfer (FRET) biosensor development and in vitro biological assay of new derivatives, (3) Cheminformatics models development and in vivo activity prediction, and (4) Docking studies. This strategy is illustrated with a case study. Firstly, a series of 4-substituted Riluzole derivatives 1-3 were synthetized through a strategy that involves the construction of the 4-bromoriluzole framework and its further functionalization via palladium catalysis or organolithium chemistry. Next, a FRET biosensor for monitoring Ca²⁺-dependent CaM-ligands interactions has been developed and used for the in vitro assay of Riluzole derivatives. In particular, the best inhibition (80%) was observed for 4-methoxyphenylriluzole 2b. Besides, we trained and validated a new Networks Invariant, Information Fusion, Perturbation Theory, and Machine Learning (NIFPTML) model for predicting probability profiles of in vivo biological activity parameters in different regions of the brain. Next, we used this model to predict the in vivo activity of the compounds experimentally studied in vitro. Last, docking study conducted on Riluzole and its derivatives has provided valuable insights into their binding conformations with the target protein, involving calmodulin and the SK4 channel. This new combined strategy may be useful to reduce assay costs (animals, materials, time, and human resources) in the drug discovery process of calmodulin inhibitors.

Developing predictive models for drug efficacy is challenged by the complexity and heterogeneity of bioassay data. Here, we present LIFE.PTML, which is a methodology integrating drug Lifecycle (L), Information Fusion (IF), Encoding (E), Perturbation Theory (PT), and Machine Learning (ML) to predict compound activity across diverse experimental conditions. Using a dataset of 3748 molecule–assay combinations targeting calmodulin (CaM) and related proteins, LIFE.PTML combines chemical and protein descriptors, quantifies experimental variability via perturbation operators, and trains non-linear classifiers, including XGBoost and Gradient Boosting. XGBoost achieved the best performance, with 88.9% test accuracy and an ROC AUC of 0.959, while feature importance analysis highlighted contributions from both drug- and protein-level descriptors. The results demonstrate that LIFE.PTML provides a robust, flexible, and interpretable framework for predictive chemoinformatics, facilitating the integration of multi-source data for drug discovery applications.

Recently, numerous models have been developed to predict drug interactions with molecules. However, integrating diverse data sources and improving the accuracy of biological activity predictions remains a challenge. This work proposes a novel solution that addresses these limitations. Here, we have developed a machine learning model to predict the efficacy of different assays and drugs for diseases related to calmodulin. To achieve this, we have compiled a comprehensive data set including commercialized drugs and experimental compounds targeting CaM complexes. The IFPTML-XGB model achieved high predictive performance, with a test accuracy of 89.1% and a sensitivity of 89.0%, demonstrating its robustness for assay efficacy prediction. We have used the IFPTML modeling technique to identify key factors influencing these activities. We have also synthesized novel riluzole derivatives and have tested them both experimentally and computationally. Biological assays and molecular docking studies have been performed to provide a molecular-scale picture of the molecule-CaM interaction. To validate the model's utility, we tested it on these derivatives. We have found that the model correctly predicts which derivatives were the most bioactive, indicating that this framework can be used to identify promising candidates for new drug formulations. This research not only improves our understanding of CaM-related diseases, but also provides an effective framework for developing new treatments based on predictive modeling.