The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union.

Background: Machine Learning (ML) has experienced an increasing use, given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need for efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in the European Union and the role of ML in the authorization process.

Machine Learning as a Proposal for a Better Application of Food Nanotechnology Regulation in the European Union.

Aims: Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety.

Background: Cheminformatic methods are able to design and create predictive models with high rate of accuracy saving time, costs and animal sacrifice. It has been applied on different disciplines including nanotechnology.

Personalized Medicine and Medicinal Chemistry: Toward a Legal Framework in the European Union.

In the last few years, the fields of Medicinal Chemistry and especially the ones related to the so-called Personalized Medicine, have received a great attention. Significant investment and remarkable researches surround the matter; however, not all those promising advances are reaching patients as quickly as they should. The absence of an adequate regulatory framework could be of no help.

Perturbation Theory Machine Learning Models: Theory, Regulatory Issues, and Applications to Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology.

Machine Learning (ML) models are very useful to predict physicochemical properties of small organic molecules, proteins, proteomes, and complex systems. These methods may be useful to reduce the cost of research in terms of materials resources, time, and laboratory animal sacrifice. Recently different authors have reported Perturbation Theory (PT) methods combined with ML to obtain PTML (PT + ML) models.