The development of new materials based on hydroxyapatite has undergone a great evolution in recent decades due to technological advances and development of computational techniques. The focus of this review is the various attempts to improve new hydroxyapatite-based materials. First, we comment on the most used processing routes, highlighting their advantages and disadvantages. We will now focus on other routes, less common due to their specificity and/or recent development. We also include a block dedicated to the impact of computational techniques in the development of these new systems, including: QSAR, DFT, Finite Elements of Machine Learning. In the following part we focus on the most innovative applications of these materials, ranging from medicine to new disciplines such as catalysis, environment, filtration, or energy. The review concludes with an outlook for possible new research directions.

The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio reriowhen compared with VDAC1-Mus musculusand VDAC1-Homo sapiens(p < 0.05). In addition,asignificant correlation (0.66 > r² > 0.97) was observed betweenn-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R²of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available athttps://doi.org/10.6084/m9.figshare.4802320.v2.