Results 11 to 20 of about 105,165 (285)
Masked Graph Modeling for Molecule Generation [PDF]
Nature Communications, 2020 De novo, in-silico design of molecules is a challenging problem with applications in drug discovery and material design. Here, we introduce a masked graph model which learns a distribution over graphs by capturing all possible conditional distributions over unobserved nodes and edges given observed ones.
Omar Mahmood +3 more
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Back translation for molecule generation
Bioinformatics, 2021 AbstractMotivationMolecule generation, which is to generate new molecules, is an important problem in bioinformatics. Typical tasks include generating molecules with given properties, molecular property improvement (i.e. improving specific properties of an input molecule), retrosynthesis (i.e.
Yang Fan +5 more
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Improving de novo Molecule Generation by Embedding LSTM and Attention Mechanism in CycleGAN
Frontiers in Genetics, 2021 The application of deep learning in the field of drug discovery brings the development and expansion of molecular generative models along with new challenges in this field.
Feng Wang +6 more
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SILVR: Guided Diffusion for Molecule Generation
Journal of Chemical Information and Modeling, 2023 paper, 20 paper, 11 ...
Nicholas T. Runcie, Antonia S.J.S. Mey
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Powerful molecule generation with simple ConvNet
Bioinformatics, 2022 AbstractMotivationAutomated molecule generation is a crucial step in in-silico drug discovery. Graph-based generation algorithms have seen significant progress over recent years. However, they are often complex to implement, hard to train and can under-perform when generating long-sequence molecules. The development of a simple and powerful alternative
Hongyang K Yu, Hongjiang C Yu
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Molecular generation by Fast Assembly of (Deep)SMILES fragments
Journal of Cheminformatics, 2021 Background In recent years, in silico molecular design is regaining interest. To generate on a computer molecules with optimized properties, scoring functions can be coupled with a molecular generator to design novel molecules with a desired property ...
Francois Berenger, Koji Tsuda
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COMA: efficient structure-constrained molecular generation using contractive and margin losses
Journal of Cheminformatics, 2023 Background Structure-constrained molecular generation is a promising approach to drug discovery. The goal of structure-constrained molecular generation is to produce a novel molecule that is similar to a given source molecule (e.g. hit molecules) but has
Jonghwan Choi +2 more
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MORTAR: a rich client application for in silico molecule fragmentation
Journal of Cheminformatics, 2023 Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process.
Felix Bänsch +6 more
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Probabilistic generative transformer language models for generative design of molecules
Journal of Cheminformatics, 2023 AbstractSelf-supervised neural language models have recently found wide applications in the generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction.
Lai Wei +4 more
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Scientific Reports, 2022 Ethanol is a widely available carbon compound that can be increasingly produced with a net negative carbon balance. Carbon-negative ethanol might therefore provide a feedstock for building a wider range of sustainable chemicals.
Hongjiang Liu +2 more
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