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Introduction Since the 1960s, after the successful development (at least with respect to the existing technologies of that time) of the first perceptron by Frank Rosenblatt, researchers around the world have been trying to make computers more brain-like. In other words, they are constantly trying to develop programs and hardware that work much like our brains. However, creating a machine that thinks and programs like a human brain is still impossible, given the limitations of present technologies. But, we are successful in this direction up to an agreeable extent. The perceptron developed by Rosenblatt can be considered one of the first implementations of neural networks. It was a huge hardware model, rather than a Python program like today's perceptrons.  Rosenblatt's perceptron was a simple binary input-output system, with various limitations. It was not trainable for different patterns, rather but for simple image recognization. The major drawback was its inability to perfo...
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Introduction: Since the dawn of time, humankind has been enthralled by the universe, from the first observations made with the unaided eye to the ground-breaking discoveries made possible by telescopes. The creation and use of space telescopes have become essential in enabling us to see beyond the limits of Earth as our need for information about the cosmos continues to expand. Space telescopes are free from atmospheric interference and can capture dim celestial objects in incredible detail. These are benefits that cannot be matched. This article explores the significance of space telescopes, outlining their development in technology and the ground-breaking discoveries they have enabled. It also looks at the ongoing and upcoming missions that have the potential to advance human knowledge of the cosmos and open up new areas of research. History of Space Telescopes: As we are talking about Telescope so it is required to know the history of it. Observing celestial objects from outside of ...
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Deep Neural Networks for ADMET properties' prediction

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Introduction The absorption, distribution, metabolism, excretion and toxicity properties of any compound contributes to its potential as an orally administered drug. A set of criteria defined by Rule of Five or the Pfizer's Rule of Five stats that any compound meeting these criteria are more likely to have ADMET properties. The importance of these properties has increased in modern medicinal chemistry and new machine learning techniques are employed to predict these properties. The predictive ADMET models have helped in discovery of small molecules with improved safety and dose. Deep Neural Networks are showing usefulness in such predictions due to improved computational efficiency, larger datasets and adaption of image processing in the chemistry. Early Models The early DNNs employed showed improved prediction performance. Many researchers raised concerned that this improvement is in fact has no relation with better computational capacity, rather it is result of mere memorizing of...
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