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Two independent artificial neural network (ANN) models were used to determine the optimal drug combination of zeolite-based delivery systems (ZDS) for cancer therapy. The systems were based on the NaY zeolite using silver (Ag+) and 5-fluorouracil (5-FU) as antimicrobial and antineoplastic agents. Different ZDS samples were prepared, and their characterization indicates the successful incorporation of both pharmacologically active species without any relevant changes to the zeolite structure. Silver acts as a counterion of the negative framework, and 5-FU retains its molecular integrity. The data from the A375 cell viability assays, involving ZDS samples (solid phase), 5-FU, and Ag+aqueous solutions (liquid phase), were used to train two independent machine learning (ML) models. Both models exhibited a high level of accuracy in predicting the experimental cell viability results, allowing the development of a novel protocol for virtual cell viability assays. The findings suggest that the incorporation of both Ag and 5-FU into the zeolite structure significantly potentiates their anticancer activity when compared to that of the liquid phase. Additionally, two optimal AgY/5-FU@Y ratios were proposed to achieve the best cell viability outcomes. The ZDS also exhibited significant efficacy against Escherichia coli(E. coli) and Staphylococcus aureus(S. aureus); the predicted combination ratio is also effective against S. aureus, underscoring the potential of this approach as a therapeutic option for cancer-associated bacterial infections.

Molecular hybridization is a well-exploited medicinal chemistry strategy that aims to combine two molecules (or parts of them) in a new, single chemical entity. Recently, it has been recognized as an effective approach to design ligands able to modulate multiple targets of interest. Hybrid compounds can be obtained by linking (presence of a linker) or framework integration (merging or fusing) strategies. Although very promising to combat the multifactorial nature of complex diseases, the development of molecular hybrids faces the critical issues of selecting the right target combination and the achievement of a balanced activity towards them, while maintaining drug-like-properties. In this review, we present recent case histories from our own research group that demonstrate why and how molecular hybridization can be carried out to address the challenges of multitarget drug discovery in two therapeutic areas that are Alzheimer’s and parasitic diseases. Selected examples spanning from linker- to fragment- based hybrids will allow to discuss issues and consequences relevant to drug design.