The full title of this conference is the MOL2NET International Conference on Multidisciplinary Sciences. This is an International Conference to Foster Interdisciplinary Collaborations in Experimental Chemistry (all branches), Medicine, Nanotechnology, Data Analysis, Bioinformatics, and Networks Sciences. MOL2NET (the conference's running title) is the acronym of the lemma of the conference: From Molecules to Networks. This running title is inspired by the possibility of multidisciplinary collaborations in science between experimentalists and theoretical scientists; represented disciplines will encompass the molecular and biomedical sciences, social networks analysis, and beyond. More specifically, this conference aims to promote scientific synergies between groups of experimental molecular and bio-medical scientists. Relevant fields include Chemistry, all areas (Inorganic Chemistry, Organic Chemistry, Medicinal Chemistry, Analytical Chemistry, Chemical Engineering), Pharmaceutical Sciences, Pharmacology, Cancer Research, OMICS, Neurosciences, Nanosciences, Materials Science, Medicine, and Biomedical Engineering, Cancer Research. Moreover, the conference welcomes computational and social sciences experts from different areas, such as Computational Chemistry, Bioinformatics, Networks Science, Social Networks analysis, Data and Computer Sciences, Predictive analytics, Biostatistics, etc. The Scientific Headquarters (HQs) are in the Faculty of Science and Technology, University of Basque Country (UPV/EHU), Bizkaia. The participation and publication of communications is online via the platform SciForum of the Editorial Molecular Diversity Preservation International (MDPI), with HQ in Basel, Switzerland, and Beijing - Wuhan, China.

The conference per se is the result of the synergy between the Department of Organic Chemistry II, UPV/EHU, and IKERBASQUE, Basque Foundation for Sciences, with the Faculty of Informatics, University of Coruña (UDC). MDPI Sciforum platform (https://sciforum.net/) will publish accepted communications online. In parallel, we are editing one special issue for International Journal of Molecular Sciences (IJMS) journal of editorial MDPI (http://www.mdpi.com/). The revision process is totally independent, please contact the editorial if you are interested.

We also invite all colleagues to share the conference website through social media. We are uploading flyers, conferences, and promotional videos in different languages to the MOL2NET accounts in different social networks.

- GOOGLE+ account with +30000 viewers: http://bit.do/gmol2net
- FACEBOOK group with +8000 followers: http://bit.do/fbmol2net
- TWITTER account: @mol2net

Sincerely yours,

Conference Chair

Prof. Humberto Gonzalez-Diaz, PhD., Pharm.Lic.

IKERBASQUE Professor of Department of Organic Chemistry II,
University of Basque Country UPV/EHU, Campus Bizkaia
Chair Endowed by Basque Government/Eusko Jaurlaritza foundation
IKERBASQUE, Basque Foundation for Science, Bilbao, Bizkaia

In nature, protein chain interactions (Pro-ChInt) of single- / multi-protein, a common but complex system, refer to physical contacts established between two or more protein chains depending on the amino acid sequences, which contains tremendous information. Encoding amino acid sequence information of protein using complex networks or graphs of the peptides is a grateful solution to discover the communication information between different Pro-ChInt. We first constructed some python codes to directly download the specify protein sequences from the RCSB protein data bank (PDB). Then, we changed the FASTA format to S2SNet format to calculate the embedded / non-embedded parameters of protein chains according to the star graph topological indices of peptide sequences. Meanwhile, we numbered all protein chains, then used the chain numbers to get a random number for a given set of chain number or case number used for each protein. Then, we replaced all the random numbers with the corresponding parameters of each protein chain calculated with S2SNet application. After that, a machine learning classification model was constructed based on the combinatorial / combining interaction of different chains. This new method can be used to identify two or more protein chain interactions combined with machine learning technique.

During the last years, research on coordination chemistry of platinum has aroused great interest due to their potential biological applications. Herein, we report the interaction of PtCI₄^2- with different polyazacyclophanes containing a pyridine unit as aromatic spacer. Formation of complexes has been studied by ¹H and ¹⁹⁵Pt NMR spectroscopy. Analysis of the recorded spectra of D₂O solutions containing L and PtCl₄^2- in a 1:1 molar ratio at acidic pH shows the evolution with time of the ¹H and ¹⁹⁵Pt signals. Different crystal structures have been solved by X-ray diffraction analysis. At acidic pHs, the metal ion is coordinated by the central amino group of the macrocyclic cavity and three chloride or bromide atoms, in a square planar geometry. Formation of [Pt(H₂L1)Br₃]Br (1) and [Pt(H₂L2)Br₃]Br (2) reveals the rapid substitution of chloride ligands in PtCl₄^2- by bromide ligands. However, as reveals the crystal structure obtained for [Pt^IVL3Br₂](PtBr₄)(H₂O) (4), at slightly higher pH values, the metal ion is coordinated through all nitrogen atoms of the macrocyclic cavity and an oxidation to Pt(IV) occurs.

Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH variations or the presence of metal cations. In our case, the conformations change varies between the so called ‘open’ and ‘closed’, the last being found at lower pH. In this study, the relative stabilities of these conformations were predicted computationally by the Density Functional Theory approximation and the reorganization from closed to open was simulated by using the Monte Carlo Multiple Minimum method.

Model validation has become a topic of great interest to many fields such as industry, medicine or even to government. Its main challenge is to provide stable and credible tools so that the decision-maker with the information necessary can make high-consequence judgments.  This process requires simulation modelling and consequently, some guidelines or evaluation criteria are essential in order to draw meaningful conclusions. A computer-aided SAS® macro is developed using the SAS/IML programming language.  Researchers should provide the dataset to be analyzed and the true values to be compared. As a result, the statistical program shows measures (i.e., number of simulations to be performed, bias, accuracy, coverage, etc…) which help investigators to make decisions with a minimal effort of programming.  Numerical results of the aforementioned statistical parameters, plots and a report are returned by the statistical tool. Although this macro is focused on the missingness setting, it is applicable to any other discipline. We encourage researchers to use it to make better statistical assessments of the used methods.

Molecular imprinted polymers (MIP) allows the preparation of tailored and high specific materials able to recognize a specific template. In this work, we simulated the affinity of a new high selective MIP able to specifically bind the isobutylphenylpropanoic acid (ibuprofen, template molecule). We have performed a series of molecular dynamics (MD) simulations of different mixtures in order to undercover the mechanisms occurring during the process of molecular imprinted polymers. The simulations were performed using the GROMACS 5.0 and the the OPLS-AA force field were used to parameterize and verifiy the studied molecules. A single system were simulated representing the pregelification state of the system. The radial distribution function (RDF) analysis and cluster analysis were used to evaluate the affinity of the template molecule, ibuprofen, for the gel backbone. Results confirm that the new material is high-selective and MD simulations are essential to study the molecular imprinting process because can give a deeper knowledge of the mechanism occurring during the imprinting process.