USEDAT Training Prog

Nanosystems are gaining momentum in pharmaceutical sciences because of the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer cotherapy drug-vitamin release nanosystems (DVRNs) including anticancer compounds and vitamins or vitamin derivatives have revealed encouraging results. However, the number of possible combinations of design and synthesis conditions is remarkably high. In addition, a large number of anticancer and vitamin derivatives have been already assayed, but a notably less number of cases of DVRNs were assayed as a whole (with the anticancer compound and the vitamin linked to them). Our approach combines with the perturbation theory and machine learning (PTML) model to predict the probability of obtaining an interesting DVRN by changing the anticancer compound and/or the vitamin present in a DVRN that is already tested for other anticancer compounds or vitamins that have not been tested yet as part of a DVRN. In a previous work, we built a linear PTML model useful for the design of these nanosystems. In doing so, we used information fusion (IF) techniques to carry out data enrichment of DVRN data compiled from the literature with the data for preclinical assays of vitamins from the ChEMBL database. The design features of DVRNs and the assay conditions of nanoparticles (NPs) and vitamins were included as multiplicative PT operators (PTOs) to the system, which indicates the importance of these variables. However, the previous work omitted experiments with nonlinear ML techniques and different types of PTOs such as metric-based PTOs. More importantly, the previous work does not consider the structure of the anticancer drug to be included in the new DVRNs. In this work, we are going to accomplish three main objectives (tasks). In the first task, we found a new model, alternative to the one published before, for the rational design of DVRNs using metric-based PTOs. The most accurate PTML model was the artificial neural network model, which showed values of specificity, sensitivity, and accuracy in the range of 90-95% in training and external validation series for more than 130,000 cases (DVRNs vs ChEMBL assays). Furthermore, in the second task, we used IF techniques to carry out data enrichment of our previous data set. In doing so, we constructed a new working data set of >970,000 cases with the data of preclinical assays of DVRNs, vitamins, and anticancer compounds from the ChEMBL database. All these assays have multiple continuous variables or descriptors d k and categorical variables c j (conditions of the assays) for drugs (dack, cacj), vitamins (dvk, cvj), and NPs (dnk, cnj). These data include >20,000 potential anticancer compounds with >270 protein targets (cac1), >580 assay cell organisms (cac2), and so forth. Furthermore, we include >36,000 assay vitamin derivatives in >6200 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit), and so forth. The enriched data set also contains >20 types of DVRNs (c5n) with 9 NP core materials (c4n), 8 synthesis methods (c7n), and so forth. We expressed all this information with PTOs and developed a qualitatively new PTML model that incorporates information of the anticancer drugs. This new model presents 96-97% of accuracy for training and external validation subsets. In the last task, we carried out a comparative study of ML and/or PTML models published and described how the models we are presenting cover the gap of knowledge in terms of drug delivery. In conclusion, we present here for the first time a multipurpose PTML model that is able to select NPs, anticancer compounds, and vitamins and their conditions of assay for DVRN design.

USA-Europe Data Analysis Training School (USEDAT) is a Multi-center Trans-Atlantic initiative offering hands-on training focused in both Introduction to Experimental Data Recording (NMR, MS, IR, 2DGE, EEG, etc.) and/or posterior Computational Data Analysis (Machine Learning, Complex Networks, etc.). We made emphasis on applications in for Cheminformatics, Bioinformatics, Medicinal Chemistry, Nanotechnology, Systems Biology, Biomedical Engineering, etc. The school also promotes training and knowledge of ethical and legal regulatory issues (GDPR, REACH, OECD, FDA, etc.) about data use and data protection in chemistry and biomedical research. The school is directed to researchers and students worldwide. Please, read the editorial paper: USEDAT: USA-Europe Data Analysis Training Worldwide Program, 2019 ed. Jerzy Leszczynski, David Quesada , Eugene Muratov, Matthew Montemore, James Robert Green , Suhbash Basak , Bakhtiyor Rasulev , Cristian Munteanu , Alejandro Pazos , Yasset Perez-Riverol , Gloria Castellano , Maité Sylla , Daniel Do Santos , Natália Cordeiro , Bairong Shen , Pedro L. Fernandes, Esther Lete , Nuria Sotomayor, Sonia Arrasate , Aliuska Duardo, Humbert Gonzalez-Diaz, MOL2NET, 2019, doi: 10.3390/mol2net-05-06254, https://sciforum.net/paper/view/conference/6254

USA-Europe Data Analysis Training School (USEDAT) is a Multi-center Trans-Atlantic initiative offering hands-on training focused in both Introduction to Experimental Data Recording (NMR, MS, IR, 2DGE, EEG, etc.) and/or posterior Computational Data Analysis (Machine Learning, Complex Networks, etc.). We made emphasis on applications in for Cheminformatics, Bioinformatics, Medicinal Chemistry, Nanotechnology, Systems Biology, Biomedical Engineering, etc. The school also promotes training and knowledge of ethical and legal regulatory issues (GDPR, REACH, OECD, FDA, etc.) about data use and data protection in chemistry and biomedical research. The school is directed to researchers and students worldwide. https://mol2net-05.sciforum.net/usedat-07

Conference: Proceedings of the conference MOL2NET International Conference on Multidisciplinary Sciences (4th edition), 2018 is part of a year-round worldwide conference series hosted by MDPI Sciforum, Basel, Switzerland. This conference series has had organized more than 20 associated workshop series in universities worldwide: USA, France, Portugal, Spain, China, Chile, Brazil, India, etc. These workshop series run in person and/or online. Some of these workshops are the SRI-10 St Thomas University (STU)- Miami Dade College (MDC), Miami, USA; USINEWS-02 University of Minnesota, USA; BIOCHEMPHYS-01 CNAM, Paris, France; WCUCW, West Coast University, Miami, USA; IWMEDIC UDC, Coruña, Spain, LAWSCI-02, UPV/EHU, Bilbao, Spain, etc. Workshops allow both in person and/or online only publication of papers, research highlights of previous papers, letters, short reviews, etc. Topics: The topics are multidisciplinary covering, but not limited to, Chemistry (All areas), Physics, Biology, Ecology, Statistics, Bioinformatics, Education, Nanotechnology, Materials, Computational, Complex Networks, Legal, and Social sciences, etc. Statistics: This edition hosted >10 workshops that attracted >300 communications submitted by >700 authors. We organized 7 special issues published in JCR journals (MDPI editorial) such as Molecules, Entropy, and Appl. Sci. We also organized 3 bootcamps, hand-training, or capstone courses in MDC, Miami, WCU Miami, and UPV/EHU Bilbao. The present book of proceedings have released in two versions; one with 94 pages (short version without communications) and other with 2985 pages (long version including all communications and abstracts). Thank you very much to all colleagues for your kind support.

In this work, we developed a general Perturbation Theory and Machine Learning (PTML) method for data mining of proteomes, in order to discover new B-cell epitopes useful for vaccine design. The method predicts the epitope activity εq(cqj) of one query peptide (q-peptide) in a set of experimental query conditions (cqj). The method uses as input the sequence of the q-peptide. The method also uses as input information about the sequence and epitope activity εr(crj) of a peptide of reference (r-peptide) assayed on similar experimental conditions (crj). The model proposed here is able to classify 1,048,190 pairs of query and reference peptide sequences from the proteome of many organisms reported on IEDB database. These pairs have variations (perturbations) in sequence or assay conditions. The model has accuracy, sensitivity, and specificity between71% and 80% for training and external validation series. The retrieved information contains structural changes in 83683 peptides sequences (Seq) determined in experimental assays with boundary conditions involving 1448 Epitope Organisms (Org), 323 Host Organisms (Host), 15 types of In vivo Process (Proc), 28 Experimental Techniques(Tech), and 505 Adjuvant additives(Adj). Afterwards, we reported the experimental sampling, isolation, and sequencing of 15 complete sequences of Bm86 gene from state of Colima, Mexico. Last, we used the model to predict the epitope immunogenic scores in different experimental conditions for the 26112 peptides obtained from these sequences. The model may become a useful tool for epitope selection towards vaccine design. The theoretic-experimental results on Bm86 protein may help on the future design of a new vaccine based on this protein.