Nanoinformatics in Drug Delivery

The use of nanomedicine for targeted drug delivery, though well established, is still a growing and developing field of research with potential benefits to many biomedical problems. There is a plethora of nano-carriers with myriads of designs of shapes, sizes and composition that involves complex, trial and error based preparation protocols. The digital age brought an information revolution with automated data analysis, machine learning and data mining applied to almost every field of research including drug delivery. Indeed, nanomedicine has benefitted from the use of data science and information science to optimize, standardize, and understand the synthesis, characterization, and biological effects of nanomaterials. This short review will describe several concepts and a few examples of nanoinformatics, including Nano-Quantitative Structure-Activity Relationship (Nano-QSAR), the use of computational methods for predicting different properties of nanomedicine in drug delivery and propose an outlook for the future.     

Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances

The application of machine learning (ML) techniques to metal-based nanomaterials has contributed greatly to understanding the interaction of nanoparticles, properties prediction, and new materials discovery. However, the prediction accuracy and efficiency of distinctive ML algorithms differ with different metal-based nanomaterials problems. This, alongside the high dimensionality and nonlinearity of available datasets in metal-based nanomaterials problems, makes it imperative to review recent advances in the implementation of ML techniques for these kinds of problems. In addition to understanding the applicability of different ML algorithms to various kinds of metal-based nanomaterials problems, it is hoped that this work will help facilitate understanding and promote interest in this emerging and less explored area of materials informatics. The scope of this review covers the introduction of metal-based nanomaterials, several techniques used in generating datasets for training ML models, feature engineering techniques used in nanomaterials-machine learning applications, and commonly applied ML algorithms. Then, we present the recent advances in ML applications to metal-based nanomaterials, with emphasis on the procedure and efficiency of algorithms used for such applications. In the concluding section, we identify the most common and efficient algorithms for distinctive property predictions. The common problems encountered in ML applications for metal-based nanoinformatics were mentioned. Finally, we propose suitable solutions and future outlooks for various challenges in metal-based nanoinformatics research.