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Transformer-based m...
Transformer-based molecular optimization beyond matched molecular pairs
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- He, Jiazhen (författare)
- AstraZeneca AB
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- Nittinger, Eva (författare)
- AstraZeneca AB
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- Tyrchan, Christian (författare)
- AstraZeneca AB
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- Czechtizky, Werngard (författare)
- AstraZeneca AB
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- Patronov, Atanas (författare)
- AstraZeneca AB
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- Bjerrum, Esben Jannik (författare)
- AstraZeneca AB
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- Engkvist, Ola, 1967 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,AstraZeneca AB
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(creator_code:org_t)
- 2022-03-28
- 2022
- Engelska.
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946 .- 1758-2946. ; 14:1
- Relaterad länk:
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https://research.cha... (primary) (free)
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
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- Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist's intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Matematik -- Annan matematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Other Mathematics (hsv//eng)
- NATURVETENSKAP -- Kemi -- Teoretisk kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences -- Theoretical Chemistry (hsv//eng)
Nyckelord
- Matched molecular pairs
- Molecular optimization
- Transformer
- Tanimoto similarity
- Scaffold
- ADMET
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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