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Does Neural Networks help in inventing Anti-Cancer Medications?





Scientists have for the first time applied a generative neural network to create new pharmaceutical medicines with the desired characteristics. By using Generative Adversarial Networks (GANs) developed and trained to "invent" new molecular structures, there may soon be a reduction in the time and cost of searching for substances with potential medicinal properties. The researchers intend to use these technologies in the search for new medications within various areas from oncology and even anti-infective.

Currently, the inorganic molecule base contains hundreds of millions of substances, and only a small fraction of them are used in medicinal drugs. The pharmacological methods of making drugs generally have a hereditary nature. For example, pharmacologists might continue to research aspirin that has already been in use for many years, perhaps adding something into the compound to reduce side effects or increase efficiency, yet the substance still remains the same.

Generative Adversarial Auto encoder (AAE) architecture, development of Generative Adversarial Networks, might have been taken as that basis, furthermore compounds with known medicinal properties and efficient concentrations were used to prepare the system. Data on these types of compounds were input into the network, which might have been afterward balanced in a way that same information might have been obtained in the yield. The system itself might have been made up of three structural elements: an encoder, decoder, and furthermore discriminator, every from claiming which required its identity or part in "cooperating" with the opposite two. The encoder worked with the decoder to compress and then restore information on the parent compound, while the discriminator helped make the compressed presentation more suitable for subsequent recovery.


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