The Advent of Generative Chemistry
Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with...
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| Vydané v: | ACS medicinal chemistry letters Ročník 11; číslo 8; s. 1496 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
13.08.2020
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| ISSN: | 1948-5875, 1948-5875 |
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| Abstract | Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for
molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry. |
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| AbstractList | Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry. Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry. |
| Author | Zhavoronkov, Alex Vanhaelen, Quentin Lin, Yen-Chu |
| Author_xml | – sequence: 1 givenname: Quentin surname: Vanhaelen fullname: Vanhaelen, Quentin organization: Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong – sequence: 2 givenname: Yen-Chu surname: Lin fullname: Lin, Yen-Chu organization: Insilico Taiwan, Taipei City 115, Taiwan, R.O.C – sequence: 3 givenname: Alex surname: Zhavoronkov fullname: Zhavoronkov, Alex organization: Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32832015$$D View this record in MEDLINE/PubMed |
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