In a recent work we described the organic synthesis and experimental pharmacological assay of MIF-1 peptidomimetics modulators of D₂ receptors (D₂R). We measured their ability to enhance the maximal effect of tritiated N-propylapomorphine ([³H]-NPA) at D₂R. The 2-furoyl-l-leucylglycinamide (6a) showed increase maximal [³H]-NPA response at 10 pM (11 ± 1%) compared to MIF-1. Neurotoxicity assays of MIF-1 derivative 6a with cortex neurons of Wistar-Kyoto rat embryos suggest low neurotoxicity. Additionally, we reported a predictive model >20 000 outcomes of preclinical assays reported in ChEMBL for this kind of modulators. The model shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. The model is useful to predict this and similar compounds.

Ref: Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML). Sampaio-Dias IE, Rodríguez-Borges JE, Yáñez-Pérez V, Arrasate S, Llorente J, Brea JM, Bediaga H, Viña D, Loza MI, Caamaño O, García-Mera X, González-Díaz H.ACS Chem Neurosci. 2021 Jan 6;12(1):203-215. doi: 10.1021/acschemneuro.0c00687. Epub 2020 Dec 21.PMID: 33347281

Warfarin is an oral anticoagulant that is widely prescribed worldwide, and it has a large individual variability. Many factors contribute to the variability of warfarin. Although numerous algorithms of warfarin have also been developed by pharmacokinetics and pharmacodynamics (PKPD) and multivariate linear regression (MLR) models, there also exits some unknown factors that could affect the warfarin anticoagulant. In this study, the precision medication model of warfarin will be established based bioinformatics in cardiac surgery patients. About 200 cardiac surgery patients who will be administered warfarin will be enrolled. The demographic characters, combined drug and physiological factors will be collected from patients’ medical records. Some of single nucleotide polymorphisins (SNP) linked with warfarin PK and PD for pharmacogenetics will be performed using pyrosequencing. Fecal samples will be used for analyzing gut microbiota by 16S rDNA. S-warfarin, R-warfarin and vitamin K concentrations, and metabolomics will be detected by LC-MS/MS. International normalized ratio (INR) is the effective efficacy index for warfarin, and target INR should be within 1.5-2.5. All data will be analyzed by machine learning or neural networks. Expected the polymorphisms of pharmacogenomics (CYP2C9, VKORC1, and CYP4F2 etc.), vitamin K concentrations, and other biomarkers from gut microbiota and metabolomics were confirmed as the effect factor for the individual variability of warfarin. And an artificial intelligence model of warfarin would be established.

Currently, diseases transmitted by the aedes aegypti mosquito in tropical areas have become a great risk to public health in these areas, within these diseases we find members of the Flaviviridae family, the Dengue Virus is the most known among them. Currently, there are other members such as: Chikungunya (CHIKV), Yellow Fever Virus (YFV), ZIKA Virus (ZIKV), etc. Although these diseases are not new in the present, there is no specific treatment to deal with them. Several of these diseases are classified as serious by the World Health Organization - WHO, taking hundreds of lives today. From phylogenetic studies it is known that members of this family possess highly conserved sequences making them an optimal target for drug development. The drug discovery model is not something new, it has traditionally been carried out under the exercise of "trial and error" and, given the arrival of new and increasingly resistant diseases, it was necessary develop new methodologies to accelerate this process.

Computational chemistry was born from the need to reduce costs, reduce time in drug development and improve the discovery of new compounds, under these three requirements over the years several techniques such as QSAR methods were the fundamental axis of chemoinformatics, With the arrival of the Big Data era, a range of possibilities opens up for the study and development of drugs, such as the implementation of the perturbation theory (PT) and machine learning (ML) models - PTML. Through the use of databases such as ChEMBL it is possible to generate a sufficient data set for the development of a prediction model using PT operators, which are based on moving averages of multiple conditions (Moving Average), which combine the characteristics and simplify data management. In this study, several PTMLLDA prediction models were evaluated based on 47815 tests obtained from ChEMBL, taking as input variables: the reference function, three molecular descriptors: AlogP, MW, TPSA, six test conditions and the interaction between the disturbance conditions and operators. Three different PTML-LDA models were evaluated under different treatments and data processing, the proposed model presents precision values of 77.25%, on the contrary, models Nº2 and Nº3 did not exceed the 77% range in their training stages. The model was validated by ROC curve obtaining a value of 86.2% indicating that the discrimination is exact and not a random pattern. The proposed PTML-LDA model was selected with a specificity of 75.95% and a sensitivity of 78.88% (see table 9). With these values, the evaluation of the model obtained by using its resulting equation (See Eq. 12) was carried out, compared to 45 new compounds synthesized by our research group, obtaining 8 compounds with high probabilities of presenting activity against this type of diseases.

Currently drug discovery is a widely used tool in the pharmaceutical and medical industry, traditionally this was a trial and error method making the processes long and expensive, for this reason the development of virtual screening techniques based on the structure arises as one of the tools to speed up this process.
Zika has been considered a serious disease according to the WHO since 2016, due to its effects in neonates who presented microcephaly and Guillan Barre syndrome in other patients. During the investigation, a structure-based virtual screening was used to identify potential inhibitors of the enzymes protease and methyltransferase of ZIKV, the methodology used arose from a combination of several energy scoring functions using three different molecular coupling programs or Docking software’s : Dock6, GOLD and OpenEye.
In selecting the best combination of functions, 32 compounds that were reported as active for NS2B-NS3 Protease and 50 compounds for NS5 MethylTransferase were used. Using decoy compounds, the method was trained so that together with the ligands they were coupled to the respective enzymes and generated potentially active molecules for these enzymes where 15632 structures with favorable values were obtained. In the search to improve the methodology, a combination of "score" functions were implemented that maximized the enrichment of the compounds. Using the programs described above, it was determined that a combination of the functions 2-4-6 assigned from these molecular coupling software’s significantly improved the enrichment values of the molecules.
Subsequently, the methodology was evaluated to determine if this combination favors enrichment by calculating the BEDROC and the enrichment factor "EF". During this analysis, it was found that at 1% of the screening recovered three active compounds for NS2B-NS3 and four compounds for NS5. This indicated that the method works, and that the combination of the selected enrichment functions favors the discovery of new drug candidates that inhibit ZIKA.

In a previous work we discussed about the concerns emerging worlwide related to the now widespread use of CRISPR-Cas genome editions' technique. This technique is a very important tool in molecular biology now a days and is called to become an important tool in Molecular Pharmacology, Personalized Medicines, and Synthetic Biology. In Drug Discovery and Medicinal Chemistry and Chemical Biology may be used for resistance-selection studies of antimicrobial compounds; research on druggability of new compounds, or implementation of new laboratory animal models for assay of new compounds. The implications for Biotechnology an dSynthetic Biology are more bizarre. Totally new compounds not existing in nature may be created in a very fast-track way. In our previouspaper, we given an state-of-art discussion of literature with examples of CRISPR uses in chemical biology. We also discussed legal and ethical concerns still preset nowadays.

Ref: A. Duardo-Sanchez. CRISPR-Cas in Medicinal Chemistry: Applications and Regulatory Concerns Curr Top Med Chem, 2017;17(30):3308-3315.

The present study mainly aims at clustering of pre-monsoon thunderstorm (TS) and non thunderstorm (NTS) days over Kolkata (22⁰32´ N, 88⁰20´ E) (India) using hard k-mean technique, backward selection procedure and fuzzy c- mean algorithm (FCM). The study involves the numerical values of the parameters observed at 0000 UTC and is performed in two stages. In the first stage , the hard c-mean technique is applied to cluster the days of a semi-supervised data set in the above mentioned two categories and the backward selection procedure is used to find the best possible combination of the theoretically influential atmospheric parameters that play the dominant role in the categorization on basis of performance score (PC). Though FCM the technique is usually applied to supervised data set, but here, in the second stage of this study, this technique is applied to the semi-supervised data set of parameters to justify the result obtained in the first stage.

The final iteration in the first stage shows that the combination of maximum vertical velocity and P-PLCL at 1000 hpa level performs best in detecting the thunderstorm days so far the present data set is concerned. It is interesting to note that this finding is also supported by FCM in the second stage of the study, where in the final iteration the center of the cluster consisting of thunderstorm days moves closer to the parameters , maximum vertical velocity and P-P_LCL at 1000 hpa level (the parameters, P and P_LCL represent respectively the pressure at the reference level and that at the corresponding lifting condensation level which is also considered as the cloud base) than that of the other cluster containing the non- thunderstorm days.

The electrocardiogram (ECG) is a fundamental tool in daily clinical medicine practice, recording millions of ECGs annually. Computer-assisted interpretation is becoming more and more important in clinical ECG processing and interpretation, serving as a crucial adjunct to physician interpretation in many clinical settings. But the existing commercial ECG interpretation algorithms still show substantial rates of misdiagnosis, and there is still a lack of comprehensive evaluation of computer-aided interpretation. We recommend using an ensemble method to process and classify clinical ECGs for providing accuracy and increasing rhythm classes.

CHEMBIOMOL-01: Chem. Biol. & Med. Chem. Workshop, Galveston, Texas-Harvard, Boston, USA, 2020 is an inter-university workshop. It is aimed to become an online reference international science workshop series on both experimental and computational Biomolecular and Biomedical Sciences. On one side the workshop is chaired and co-hosted by professors of the Dept. of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, and the Department of Earth and Planetary Sciences, Harvard University (HARVARD), Boston, USA. On the other side, the workshop is chaired and co-hosted by professors of the Dept. of Pharmacology and Toxicology (PHTOX), University of Texas Medical Branch, Galveston, USA. This first edition is dedicated to Dr. Allen Reitz for his successful activity as Founder Editor in Chief of the journal Current Topics in Medicinal Chemistry. This workshop is associated to the MOL2NET International Conference Series on Multidisciplinary Sciences. MOL2NET (From Molecules to Networks) series is running this year MOL2NET-2020, International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland, 2020. Consequently, it is co-chaired and promoted as well by researchers of the University of The Basque Country (UPV/EHU) and IKERBASQUE, Basque Foundation for Science, Bilbao, Basque Country, Spain. The link of the workshop is: https://mol2net-06.sciforum.net/chembiomol-01

Phenolic compounds are considered as dangerous pollutants, which produces serious environmental problems by pollution of water streams because of their great water solubility and high toxicity. In this paper we present the modeling of inhibitory grown activity against Tetrahymena pyriformis with structural feature descriptors. Quantitative structure-toxicity relationship model for acute toxicity of phenol derivatives was performed using Multiple Linear Regression (MLR), Reduced Error Pruning Tree (REPTree), M5 Model Rules (M5R), Multilayer Perceptron (MLP), Instance-Based Learning algorithms using K nearest neighbor (IBk-ANN), Support vector machine (SVM), and Radial basis function network (RBF). The correlation coefficients (R²) of training sets and test sets were 0.88 and 0.86 for the best MLR model, 0.82 and 0.72 for the best machine learning model (SVM), respectively. Following to the obtained results, our proposed model may be useful to predict of toxicity and risk assessment of phenol derivatives compound.

In this report, we propose the Machine Learning FURIA-C as a cutting-edge to classify drug-like compounds with anti-diabetic inhibitory ability toward the main two pharmacological targets α-amylase, and α-glucosidase. This model was tested for its classification capability over each repository, achieving satisfactory accuracy scores of 94.5% and 96.5%, respectively. Another important outcome was to achieve various α-amylase and α-glucosidase fuzzy rules with high Certainty Factor values. Some of the rules derived from the training series and active classification rules were interpreted. An important external validation step, comparing our method with the ones already reported, was included as well. The Holm test comparison showed significant differences (p-value<0.05) between Furia C versus Linear Discriminating Analysis (LDA) and Bayes Network, the former beating the last two ones. According to the relative ranking score, the out-performing technique is FURIA-C. Our analysis suggests that Furia-C could be used as a cutting-edge technique to predict (classify or screen), the α-amylase and α-glucosidase inhibitory activity, leading to the discovery of potent antidiabetic agents.