AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.     

Application of Machine Learning Algorithms With and Without Principal Component Analysis for the Design of New Multiphase High Entropy Alloys

Metallurgical and Materials Transactions A - The design of high entropy alloys (HEAs) can be accelerated using machine learning (ML) algorithms. In the current study, the design parameter’s...     

Effects of temperature and blank holding force on biaxial forming behavior of aluminum sheet alloys

Biaxial forming behavior is investigated for three aluminum sheet alloys (Al 5182 containing 1% Mn (5182+Mn), Al 5754, and 6111-T4) using a heated die and punch in the warm forming temperature range of 200–350 °C. It is found that, while all three alloys exhibit significant improvement in their formability compared with that at room temperature, the non-heat-treatable alloys 5182 + Mn and 5754 give higher part depths than that of heat-treatable 6111-T4. The formability generally increases with decreasing BHP (BHP), but increasing the forming temperature and/or BHP minimizes the wrinkling tendency and improves the forming performance. The stretchability of the sheet alloys increase with increasing temperature and increasing BHP. For the alloys and forming conditions involved in the current study, the formability, measured in terms of part depth, comes mainly from the drawing of metal into the die cavity, although stretching effects do influence the overall forming behavior. The optimum formability is achieved by setting the die temperature 50 °C higher than the punch temperature to enhance the drawing component. Setting the die temperature higher than the punch temperature also improves the strain distribution in a part in such a manner that postpones necking and fracture by altering the location of greatest thinning.     

A unified method for optimal arbitrary pole placement

We consider the classic problem of pole placement by state feedback. We offer an eigenstructure assignment algorithm to obtain a novel parametric form for the pole-placing feedback matrix that can deliver any set of desired closed-loop eigenvalues, with any desired multiplicities. This parametric formula is then exploited to introduce an unconstrained nonlinear optimisation algorithm to obtain a feedback matrix that delivers the desired pole placement with optimal robustness and minimum gain. Lastly we compare the performance of our method against several others from the recent literature.     

Design of novel compact anti-stiction and low insertion loss RF MEMS switch

A novel torsional RF MEMS capacitive switch design on silicon substrate is presented. The optimized switch topology such as reduction in up-state capacitance results in insertion loss better than ?0.1 dB till 20 GHz. Off to on state capacitance ratio is also improved by 18 fold and isolation is better than ?43 dB at 9.5 GHz. The achieved on state return loss is ?38 dB as compared to ?21 dB at 9.5 GHz. An optimized reduction in contact area and use of floating metal layer increases the switching speed from 56 to 46 μsec. It also increases the switch reliability by alleviating the stiction.     

A Simple D 2-Sampling Based PTAS for k-Means and Other Clustering Problems

Given a set of points \(P \subset\mathbb{R}^{d}\) , the k-means clustering problem is to find a set of k centers \(C = \{ c_{1},\ldots,c_{k}\}, c_{i} \in\mathbb{R}^{d}\) , such that the objective function ∑ _x∈P e(x,C)², where e(x,C) denotes the Euclidean distance between x and the closest center in C, is minimized. This is one of the most prominent objective functions that has been studied with respect to clustering. D ²-sampling (Arthur and Vassilvitskii, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA’07, pp. 1027–1035, SIAM, Philadelphia, 2007) is a simple non-uniform sampling technique for choosing points from a set of points. It works as follows: given a set of points \(P \subset\mathbb{R}^{d}\) , the first point is chosen uniformly at random from P. Subsequently, a point from P is chosen as the next sample with probability proportional to the square of the distance of this point to the nearest previously sampled point. D ²-sampling has been shown to have nice properties with respect to the k-means clustering problem. Arthur and Vassilvitskii (Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA’07, pp. 1027–1035, SIAM, Philadelphia, 2007) show that k points chosen as centers from P using D ²-sampling give an O(logk) approximation in expectation. Ailon et al. (NIPS, pp. 10–18, 2009) and Aggarwal et al. (Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, pp. 15–28, Springer, Berlin, 2009) extended results of Arthur and Vassilvitskii (Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA’07, pp. 1027–1035, SIAM, Philadelphia, 2007) to show that O(k) points chosen as centers using D ²-sampling give an O(1) approximation to the k-means objective function with high probability. In this paper, we further demonstrate the power of D ²-sampling by giving a simple randomized (1+?)-approximation algorithm that uses the D ²-sampling in its core.     

Improvement of polymer blend properties by changing sequence of mixing

The basic objective of this article is to improve the polymer blend properties by changing mixing sequence. Blending of two elastomers does not lead to a molecularly homogeneous blend (true solution), but to a heterogeneous system in which both polymer phases are present. In this article, the detailed study of heterogeneous distribution of carbon black as well as blend inhomogenity and the physicomechanical including dynamic mechanical properties of the blend has been carried out. The choice of the blend was natural rubber/polybutadiene rubber as 85:15. Heterogeneous carbon black distribution study was also performed in differential scanning calorimeter. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 2735–2742, 2007     

Towards identifying the roll motion parameters of a motorcycle simulator

This study aimed at identifying the roll motion parameters of a motorcycle simulator prototype. Experienced motorcyclists tuned the angular physical movement of the mock-up and that of the visual scene to achieve an optimal riding experience during curves. The participants exceeded the rolling angles that would be required in real-world riding, while avoiding leaning the mock-up beyond 10°. In addition, they were more influenced by the speed of the virtual motorcycle than by road curvature, especially in a wide field of view. Heterogeneity was found in the roll applied to the visual scene. The overall patterns suggest that at least when washout is not applied to remove the side forces that in real-world riding are compensated by a centrifugal force, greater roll of the visual at the expense of the mock-up is mandatory to avoid performance biases that might be enhanced due to fear of falling off the simulator. Future roll motion models must take into consideration factors such as riding postures, which might not only influence the forces operating on the rider-motorcycle system, but also how motorcyclists perceive the visual world.     

Compositional trend analysis on poly(phenylene ether) based thermoplastic elastomers

Detailed statistical trend analysis of thermoplastic elastomers based on poly (phenylene ether) (PPE), polystyrene (PS), ethylene vinyl acetate (EVA) and styrene‐ethylene‐butylene‐styrene (SEBS) was done through Design Expert software by Stat‐Ease. D optimal crossed design was followed to capture the interaction with the parameters. Effect of blend ratio, vinyl acetate (VA) content of EVA, molecular weight (MW) of SEBS and intrinsic viscosity (IV) of PPE on the blend performance (response) was studied in detail. Design of Experiment (DOE) analysis showed the “optimized formulation” of the blend. Increase in PPE‐polystyrene (PS) content increased tensile strength and modulus of the blend, followed by a decrease in strain at break. However, EVA had a reverse effect on tensile strength and modulus. Strain at break increased significantly with increasing SEBS content in the blend. Graphical and numerical optimization showed that superior mechanical properties (tensile strength, strain at break and modulus) could be achieved at VA content ～ 50% at a particular loading of EVA. Low MW SEBS was found to be more compatible with the other components of the blend. Mechanical properties of the quaternary blend were marginally affected with change in IV of PPE in the range of 0.33 to 0.46. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Synthesis of ethyl propionate catalyzed by poly(N‐AEAAm‐co‐AAc)‐cl‐MBAm hydrogel‐immobilized lipase of Bacillus coagulans MTCC‐6375

A purified alkaline thermotolerant bacterial lipase of Bacillus coagulans MTCC‐6375 was efficiently immobilized onto poly(N‐AEAAm‐co‐AAc‐cl‐MBAm)‐hydrogel at pH 8.5 and at temperature 55°C in 16 h. The hydrogel‐bound matrix possessed 1.04 U/g (matrix) lipase activity with a specific activity of 1.8 U/mg of protein. The immobilized lipase resulted in formation of 52.5 mM of ethyl propionate (52% conversion) at 55°C in 9 h in n‐nonane. Ethanol and propionic acid when used in a ratio of 300 : 100 mM, respectively, in n‐nonane along with 10 mg of hydrogel‐bound lipase resulted in optimal synthesis of ethyl propionate (82.5 mM). Addition of molecular sieves (3 Å, 0.7 g/reaction volume) further enhanced the conversion rate to 82.4% resulting in 83.5 mM of ethyl propionate. Incubation temperature below or above 55°C had a marked effect on the synthesis of ethyl propionate. However, esterification performed in n‐heptane at 65°C resulted in 87.5 mM of ethyl propionate with a conversation rate of 89.3%. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007