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.

CRISPR-Cas in Medicinal Chemistry: Applications and Regulatory Concerns.

A rapid search in scientific publication's databases shows how the use of CRISPR-Cas genome editions' technique has considerably expanded, and its growing importance, in modern molecular biology. Just in pub-med platform, the search of the term gives more than 3000 results. Specifically, in Drug Discovery, Medicinal Chemistry and Chemical Biology in general CRISPR method may have multiple applications.

ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks.

This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs.

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks.

Patents of bio-active compounds based on computer-aided drug discovery techniques.

In recent times, there has been an increased use of Computer-Aided Drug Discovery (CADD) techniques in Medicinal Chemistry as auxiliary tools in drug discovery. Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome that results is in the creation of new computational tools. This process is often accompanied by a lack of understanding of the legal aspects related to software and model use, that is, the copyright protection of new medicinal chemistry software and software-mediated discovered products.

Legal issues for chem-bioinformatics models.

Chem-Bioinformatic models connect the chemical structure of drugs and/or targets (protein, gen, RNA, microorganism, tissue, disease...

From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices.

Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure.

From chemical graphs in computer-aided drug design to general Markov-Galvez indices of drug-target, proteome, drug-parasitic disease, technological, and social-legal networks.

Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures to large systems. We can cite for instance, drug-target protein interaction networks, drug policy legislation networks, or drug treatment in large geographical disease spreading networks.

New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.

Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct.

Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues.

Quantitative Structure-Activity Relationship (QSAR) models have been used in Pharmaceutical design and Medicinal Chemistry for the discovery of anti-parasite drugs. QSAR models predict biological activity using as input different types of structural parameters of molecules. Topological Indices (TIs) are a very interesting class of these parameters.

Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers.

In this communication we carry out an in-depth review of a very versatile QSPR-like method. The method name is MARCH-INSIDE (MARkov CHains Ivariants for Network Selection and DEsign) and is a simple but efficient computational approach to the study of QSPR-like problems in biomedical sciences. The method uses the theory of Markov Chains to generate parameters that numerically describe the structure of a system.

Current topics on software use in medicinal chemistry: intellectual property, taxes, and regulatory issues.

In recent times, there has been an increased use of software and computational models in Medicinal Chemistry, both for the prediction of effects such as drug-target interactions, as well as for the development of (Quantitative) Structure-Activity Relationships ((Q)SAR). Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome that results is in the creation of new computational tools. The adoption of computational tools by medicinal chemists is sadly, and all too often accompanied, by a lack of understanding of the legal aspects related to software and model use, that is, the copyright protection of new medicinal chemistry software and software-mediated discovered products.