Sivaranjani et al.: Antimycobacterial Therapeutic Design against Shikimate Pathway In Mycobacterium tuberculosis, shikimate pathway is essential for amino acid biosynthesis, siderophores formation to overcome starvation, to survive in low oxygen conditions as well as for pathogen's virulence and growth. 3-Dehydroquinate synthase of Mycobacterium tuberculosis plays a vital role in the biosynthesis of aromatic amino acids and various secondary metabolites through shikimate pathway and is responsible for development of drug resistance. Thus, designing inhibitors towards this attractive drug target 3-dehydroquinate synthase to inhibit the synthesis of aromatic amino acids and essential secondary metabolites could prevent survival of this pathogen. In the present work, docking studies were performed using the 3-dehydroquinate synthase crystal structure against 1082 approved DrugBank compounds. The best DrugBank compound and substrate analogue (carbaphosphonate) were subjected to shape screening against 21 million compounds and resulted compounds constituted the AroB ligand-dataset. The library was subjected to rigid receptor docking, quantum polarized ligand docking, and induced fi t docking followed by molecular mechanics-generalized born and surface area calculations resulted in two compounds possess the best scoring functions (XP GScore). Molecular dynamics simulations (50 ns) in the solvated model system determined consistency nature of AroB-lead 1 over the AroB-carbaphosphonate complex. Moreover, upon comparison of the proposed leads with the best DrugBank compound and carbaphosphonate, the leads showed favourable absorption, distribution, metabolism, excretion and toxicity properties within the range of 95 % FDA approved drugs, and showing better antagonist properties than the existing inhibitors. Hence these leads were proposed as novel inhibitors against tuberculosis.

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Heart disease instances are rising at an alarming rate, and it is critical and essential to predict any such ailments in advance. This is a challenging diagnostic that must be done accurately and swiftly. Lack of relevant data is often the impeding factor when it comes to various areas of research. Data augmentation is a strategy for improving the training of discriminative models that may be accomplished in a variety of ways. Deep generative models, which have recently advanced, now provide new approaches to enrich current data sets. Generative Models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are frequently used to generate high quality, realistic, synthetic data essential for machine learning algorithms as they play a critical role in various classification problems. In our case, we were provided with 304 rows of heart disease data to create a robust model for predicting the presence of an ailment in the patient. However, the identification of heart disease would not be efficient given the small amount of available training data. We used GAN, CGAN, and VAE to generate data to tackle this problem, thus augmenting the original data. This additional data will help in increasing the accuracy of the models created using the new dataset. We applied classification-based Machine Learning models such as Logistic Regression, Decision Trees, KNN, and Random Forest. We compared the accuracy of the said models, each of which was supplied with the original dataset and the augmented datasets that used the data generation techniques mentioned above. Our research suggests that using data generation techniques significantly boosts the accuracy of the machine learning techniques applied to them.

Reinforcement learning is an artificial intelligence paradigm that enables intelligent agents to accrue environmental incentives to get superior results. It is concerned with sequential decision-making problems which offer limited feedback. Reinforcement learning has roots in cybernetics and research in statistics, psychology, neurology, and computer science. It has piqued the interest of the machine learning and artificial intelligence groups in the last five to ten years. It promises that it allows you to train agents using rewards and penalties without explaining how the task will be completed. The RL issue may be described as an agent that must make decisions in a given environment to maximize a specified concept of cumulative rewards. The learner is not taught which actions to perform but must experiment to determine which acts provide the greatest reward. Thus, the learner has to actively choose between exploring its environment or exploiting it based on its knowledge. The exploration-exploitation paradox is one of the most common issues encountered while dealing with Reinforcement Learning algorithms. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. We describe how to utilize several deep reinforcement learning (RL) algorithms for managing a Cartpole system used to represent episodic environments and Stock Market Trading, which is used to describe continuous environments in this study. We explain and demonstrate the effects of different RL ideas such as Deep Q Networks (DQN), Double DQN, and Dueling DQN on learning performance. We also look at the fundamental distinctions between episodic and continuous activities and how the exploration-exploitation issue is addressed in their context.

Parkinson’s disease (PD) is a one of the most common neurodegenerative disease affecting the central nervous system (CNS) characterized by the multitude of motor and non-motor clinical symptoms. The hallmark of PD motor manifestation include progressive tremor, rigidity, brady kinesia and postural instability. There are many protein involved in the progression of this dieses including TG2 and DJ-1 protein. The present is focused on finding the novel inhibitor based from phytochemicals catagory to inhibit activity of TG2 and DJ-1 protein. The Cheminformatics pipeline used which include ADMET analysis,pharmacophore modeling and molecular docking. Six best hit molecules were mapped with the E-pharmacophore features of TG2 and DJ-1 protein. These pharmacophore were further analysed by molecular docking, protein–ligand interactions and in silico ADMET studies. The molecular docking analysis revealed that hydroxywogonin and 2',3',5,7-Tetrahydroxy flavones had good binding energy and satisfied the Lipinski rule of five and had no toxicity.