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De Novo Drug Design...
De Novo Drug Design Using Reinforcement Learning with Graph- Based Deep Generative Models
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- Atance, Sara Romeo (författare)
- AstraZeneca AB,Chalmers tekniska högskola,Chalmers University of Technology
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- Viguera Diez, Juan, 1997 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Engkvist, Ola, 1967 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Olsson, Simon, 1985 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Mercado, Rocio, 1992 (författare)
- AstraZeneca R&D Mölndal
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visa färre...
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(creator_code:org_t)
- 2022-10-11
- 2022
- Engelska.
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-960X .- 1549-9596. ; 62:20, s. 4863-4872
- Relaterad länk:
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- Machine learning provides effective computational tools for exploring the chemical space via deep generative models. Here, we propose a new reinforcement learning scheme to finetune graph-based deep generative models for de novo molecular design tasks. We show how our computational framework can successfully guide a pretrained generative model toward the generation of molecules with a specific property profile, even when such molecules are not present in the training set and unlikely to be generated by the pretrained model. We explored the following tasks: generating molecules of decreasing/increasing size, increasing drug-likeness, and increasing bioactivity. Using the proposed approach, we achieve a model which generates diverse compounds with predicted DRD2 activity for 95% of sampled molecules, outperforming previously reported methods on this metric.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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