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METHODS, SYSTEMS AND NON-TRANSITORY COMPUTER READABLE MEDIA FOR AUTOMATED DESIGN OF MOLECULES WITH DESIRED PROPERTIES USING ARTIFICIAL INTELLIGENCE
METHODS, SYSTEMS AND NON-TRANSITORY COMPUTER READABLE MEDIA FOR AUTOMATED DESIGN OF MOLECULES WITH DESIRED PROPERTIES USING ARTIFICIAL INTELLIGENCE
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机译:利用人工智能自动设计具有所需特性的分子的方法,系统和非暂态计算机可读介质
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摘要
The subject matter described herein includes computational methods, systems and non-transitory computer readable media for de-novo drug discovery, which is based on deep learning and reinforcement learning techniques. The subject matter described herein allows generating chemical compounds with desired properties. Two deep neural networks - generative and predictive, represent the general workflow. The process of training consists of two stages. During the first stage, both models are trained separately with supervised learning algorithms, and during the second stage, models are trained jointly with reinforcement learning approach. In this study, we conduct a computational experiment, which demonstrates the efficiency of proposed strategy to maximize, minimize or impose a desired range to a property. We also thoroughly evaluate our models with quantitative approaches and provide visualization and interpretation of internal representation vectors for both predictive and generative models.
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