Aiming at reducing the unselective cytotoxicity of Pt(II) chemotherapeutics, a great deal of effort has been concentrated into the design of metal-containing drugs with different anticancer mechanisms of action. Inert Pt(IV) prodrugs have been proposed to be a valid alternative as they are activated by reduction directly into the cell releasing active Pt(II) species. On the other hand, a promising strategy for designing metallodrugs is to explore new potential biological targets rather than canonical B-DNA. G-quadruplex nucleic acid, obtained by self-assembly of guanine-rich nucleic acid sequences, has recently been considered an attractive target for anticancer drug design. Therefore, compounds capable of binding and stabilizing this type of DNA structure would be greatly beneficial in anticancer therapy. Here, computational analysis reports the mechanism of action of a recently synthesized Pt(IV)–salphen complex conjugating the inertness of Pt(IV) prodrugs with the ability to bind G-quadruplexes of the corresponding Pt(II) complex. The reduction mechanism of the Pt(IV) complex with a biological reducing agent was investigated in depth by means of DFT, whereas classical MD simulations were carried out to shed light into the binding mechanism of the released Pt(II) complex. The results show that the Pt(IV) prodrug may be reduced by both inner- and outer-sphere mechanisms, and the active Pt(II) complex, as a function of its protonation state, stabilizes the G-quadruplex DNA prevalently, either establishing π-stacking interactions with the terminal G-tetrad or through electrostatic interactions along with H-bonds formation.

Effective light-based cancer treatments, such as photodynamic therapy (PDT) and photoactivated chemotherapy (PACT), rely on compounds that are activated by light efficiently, and absorb within the therapeutic window (600–850 nm). Traditional prediction methods for these light absorption properties, including Time-Dependent Density Functional Theory (TDDFT), are often computationally intensive and time-consuming. In this study, we explore a machine learning (ML) approach to predict the light absorption in the region of the therapeutic window of platinum, iridium, ruthenium, and rhodium complexes, aiming at streamlining the screening of potential photoactivatable prodrugs. By compiling a dataset of 9775 complexes from the Reaxys database, we trained six classification models, including random forests, support vector machines, and neural networks, utilizing various molecular descriptors. Our findings indicate that the Extreme Gradient Boosting Classifier (XGBC) paired with AtomPairs2D descriptors delivers the highest predictive accuracy and robustness. This ML-based method significantly accelerates the identification of suitable compounds, providing a valuable tool for the early-stage design and development of phototherapy drugs. The method also allows to change relevant structural characteristics of a base molecule using information from the supervised approach. Scientific Contribution: The proposed machine learning (ML) approach predicts the ability of transition metal-based complexes to absorb light in the UV–vis therapeutic window, a key trait for phototherapeutic agents. While ML models have been used to predict UV–vis properties of organic molecules, applying this to metal complexes is novel. The model is efficient, fast, and resource-light, using decision tree-based algorithms that provide interpretable results. This interpretability helps to understand classification rules and facilitates targeted structural modifications to convert inactive complexes into potentially active ones.