New Insights to Understand the CoMFA Analysis within the Density Functional Theory Framework

The Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) models now have a wide range of applications; however, new methodologies are required due to the complexity in understanding their results. This research presents a generalized version of quantum similarity field and chemical reactivity descriptors within the density functional theory framework. By taking reference compounds, this generalized methodology can be used to understand the biological activity of a molecular set. In this sense, this methodology allows to study of the CoMFA in quantum similarity and chemical reactivity. It is feasible to investigate steric and electrostatic effects on local substitutions using this method. They were considering that how these methodologies could be used when the receptor is known or unknown.

Everything as an Algorithm ~ Computational Analysis and Model for Space/Time Complexity

Utilizing multiple theorems derived from and formulating the equation : Z = {∀Θ ∈ Z → ∃s ∈ P S ∧ ∃t ∈ T : Θ = (s, t)} and formulating the equation: X = O + Ĥ + (n(log)Φ Pd x ), as well as some mathematical constraints and numerous implications in Quantum Physics, Classical Mechanics, and Algorithmic Quantization, we come up with a framework for mathematically representing our universe. These series of individualized papers make up a huge part of a dissertation on the subject matter of Quantum Similarity. Everything including how we view time itself and the origin point for our universe is explained in theoretical details throughout these papers.

New insights about the serine/threonine protein kinase substrates from Mycobacterium tuberculosis using molecular docking, quantum similarity analysis and DFT calculations

Background: The protein kinases present in the human body have received a lot of attention because of the interest in their use as therapeutic targets. However, little is known about the protein kinases associated with tuberculosis. For these reasons, this research investigates a new point of view regarding the crystallized serine/threonine protein kinases Pkn A, B and G of Mycobacterium tuberculosis. Methods: The conformational analysis shows a DFG-in motif in Pkn B and G and a DFG-out motif in Pkn A. For all the protein kinases that have been studied, the gatekeeper residue is methionine. A study of the protein kinases with their ligands was also conducted to find new insights on the binding site with a series of ligands associated to protein kinases Pkn A, B and G through molecular docking. The residues with hydrogen bonds on the hinge zone of Pkn A are GLU96 and VAL 98, of Pkn B are GLU 93 and VAL 95 and of Pkn G are GLU233 and VAL235. Results: The results show the H-bond acceptor and H-bond donor sites on the hinge zone to all ligands, establishing a structural model of the ligands on the active site with two or three interactions in this zone. This interaction model was validated using density functional theory calculations (by means of net charges and images of the electrostatic potential) and molecular quantum similarity analysis, showing a high correlation between the electronic and steric effects in each ATP complex studied. Conclusions: In this work we can see that the interactions of the hinge zone are characterized by the key factor of one or two H-bonds acceptors and one H-bond donor in the ligands of this zone. The quantum similarity analysis shows good correlation between the steric and electronic effects in each ATP complex.

Quantum Similarity in our DNA, and in DNA Storage

The usage of Quantum Similarity through the equation Z = {∀θ ∈ Z → ∃s ∈ S ∧ ∃t ∈ T : θ = (s, t)}, represents a way to analyze the way communication works in our DNA. Being able to create the object set reference for z being (s, t) in our DNA strands, we are able to set logical tags and representations of our DNA in a completely computational form. This will allow us to have a better understanding of the sequences that happen in our DNA. With this approach, we can also utilize mathematical formulas such as the Euler–Mascheroni constant, regression analysis, and computational proofs to answer important questions on Quantum biology, Quantum similarity, and Theoretical Physics.

Two-sided regularization model based on probabilistic matrix factorization and quantum similarity for recommender systems

Nowadays, with the advent of the age of Web 2.0, several social recommendation methods that use social network information have been proposed and achieved distinct developments. However, the most critical challenges for the existing majority of these methods are: (1) They tend to utilize only the available social relation between users and deal just with the cold-start user issue. (2) Besides, these methods are suffering from the lack of exploitation of content information such as social tagging, which can provide various sources to extract the item information to overcome the cold-start item and improve the recommendation quality. In this paper, we investigated the efficiency of data fusion by integrating multi-source of information. First, two essential factors, user-side information, and item-side information, are identified. Second, we developed a novel social recommendation model called Two-Sided Regularization (TSR), which is based on the probabilistic matrix factorization method. Finally, the effective quantum-based similarity method is adapted to measure the similarity between users and between items into the proposed model. Experimental results on the real dataset show that our proposed model TSR addresses both of cold-start user and item issues and outperforms state-of-the-art recommendation methods. These results indicate the importance of incorporating various sources of information in the recommendation process.