MEMX (United States)

Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO models were trained under different task scenarios by recognizing syntactic patterns and structure–property relationships. We further explored the performance of NIMO in structure-guided, activity-oriented, and pocket-based molecule generation tasks. Our results show that NIMO had excellent performance for molecule generation from scratch and structure optimization from a scaffold.

Developed nanostructured α-Synuclein amyloid fibrils-Pd nanocomposite showed superior catalytic activity for C–H activation as well as electrooxidation of ethanol.

Reinforcement learning (RL) is one of the fundamental areas of machine learning and is widely regarded as a necessary component of artificial general intelligence (AGI). While RL has been researched for a few decades, the advent of deep learning has resulted in the so-called deep reinforcement learning algorithms that utilize deep neural networks and large-scale computing power to significantly improve the capabilities of RL. They have resulted in computer programs that play video games and board games at super human level and even beat handcrafted computer programs, e.g., chess program AlphaZero beat the best handcrafted program Stockfish 8. Besides video and board games, RL is being applied to enable robots to learn complex tasks in autonomous driving and industrial automation. This chapter covers the fundamental concepts of RL and the taxonomy of RL algorithms. In the next chapter, you will learn some of the key algorithms in reinforcement learning. An understanding of this chapter will provide you with a good feel for RL.

In the previous chapter, you were introduced to major aspects of reinforcement learning. This chapter takes you through the next steps on dealing with those challenges in order to form the algorithms for reinforcement learning. Reinforcement learning is an area of very active research, and new variations of algorithms are proposed regularly. An understanding of this chapter will provide you with a good basis, so that you can appreciate not just the current generation algorithms but also understand new research findings in this area. RL involves interaction of multiple components and concepts, which you have already seen in prior chapters in this book.

The learnings in this chapter will help you determine if your choice of features and the number of datasets are sufficient or should you increase datasets and/or increase/decrease the number of features forming your hypothesis.

Abstract : New approaches offer the promise of providing energy efficient, low cost, small, and highly sensitive magnetic sensors. However, the 1/f noise of these new types of sensors is a major obstacle. Many army applications, such as detecting moving targets, require sensitivity at low frequencies. This paper reports development of a device, the MEMS flux concentrator, invented at ARL, that minimizes the effect of 1/f noise in sensors. The device accomplishes this by shifting the operating frequency to higher frequencies where 1/f noise is much lower. This shift is accomplished by modulating the magnetic field before it reaches the sensor. In our device, the magnetic sensor, a GMR sensor, is placed between flux concentrators that have been deposited on MEMS flaps. The motion of the MEMS flaps modulates the field by a factor of 3 at frequencies from 8 to 15 kHz. The MEMS flux concentrator should increase the sensitivity of many magnetic sensors by two to three orders of magnitude. An equally important benefit is that, because it is a modulation technique, it eliminates the problem of dealing with the large DC bias of most magnetoresistive sensors.