The Experts below are selected from a list of 60861 Experts worldwide ranked by ideXlab platform
Woo Youn Kim - One of the best experts on this subject based on the ideXlab platform.
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scaffold based molecular design using graph Generative Model
Chemical Science, 2020Co-Authors: Jaechang Lim, Sangyeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn KimAbstract:Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph Generative Model that targets its use in scaffold-based molecular design. Our Model accepts a molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our Model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our Model to designing inhibitors of the epidermal growth factor receptor and show that our Model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.
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scaffold based molecular design using graph Generative Model
arXiv: Learning, 2019Co-Authors: Jaechang Lim, Sangyeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn KimAbstract:Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called for a strategy of designing molecules retaining a particular scaffold as a substructure. On this account, our present work proposes a scaffold-based molecular Generative Model. The Model generates molecular graphs by extending the graph of a scaffold through sequential additions of vertices and edges. In contrast to previous related Models, our Model guarantees the generated molecules to retain the given scaffold with certainty. Our evaluation of the Model using unseen scaffolds showed the validity, uniqueness, and novelty of generated molecules as high as the case using seen scaffolds. This confirms that the Model can generalize the learned chemical rules of adding atoms and bonds rather than simply memorizing the mapping from scaffolds to molecules during learning. Furthermore, despite the restraint of fixing core structures, our Model could simultaneously control multiple molecular properties when generating new molecules.
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molecular Generative Model based on conditional variational autoencoder for de novo molecular design
Journal of Cheminformatics, 2018Co-Authors: Jaechang Lim, Seongok Ryu, Jinwoo Kim, Woo Youn KimAbstract:We propose a molecular Generative Model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.
Jaechang Lim - One of the best experts on this subject based on the ideXlab platform.
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scaffold based molecular design using graph Generative Model
Chemical Science, 2020Co-Authors: Jaechang Lim, Sangyeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn KimAbstract:Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph Generative Model that targets its use in scaffold-based molecular design. Our Model accepts a molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our Model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our Model to designing inhibitors of the epidermal growth factor receptor and show that our Model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.
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scaffold based molecular design using graph Generative Model
arXiv: Learning, 2019Co-Authors: Jaechang Lim, Sangyeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn KimAbstract:Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called for a strategy of designing molecules retaining a particular scaffold as a substructure. On this account, our present work proposes a scaffold-based molecular Generative Model. The Model generates molecular graphs by extending the graph of a scaffold through sequential additions of vertices and edges. In contrast to previous related Models, our Model guarantees the generated molecules to retain the given scaffold with certainty. Our evaluation of the Model using unseen scaffolds showed the validity, uniqueness, and novelty of generated molecules as high as the case using seen scaffolds. This confirms that the Model can generalize the learned chemical rules of adding atoms and bonds rather than simply memorizing the mapping from scaffolds to molecules during learning. Furthermore, despite the restraint of fixing core structures, our Model could simultaneously control multiple molecular properties when generating new molecules.
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molecular Generative Model based on conditional variational autoencoder for de novo molecular design
Journal of Cheminformatics, 2018Co-Authors: Jaechang Lim, Seongok Ryu, Jinwoo Kim, Woo Youn KimAbstract:We propose a molecular Generative Model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.
Tomer Michaeli - One of the best experts on this subject based on the ideXlab platform.
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SinGAN: Learning a Generative Model from a Single Natural Image
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Tamar Rott Shaham, Tali Dekel, Tomer MichaeliAbstract:We introduce SinGAN, an unconditional Generative Model that can be learned from a single natural image. Our Model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.
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ICCV - SinGAN: Learning a Generative Model From a Single Natural Image
2019 IEEE CVF International Conference on Computer Vision (ICCV), 2019Co-Authors: Tamar Rott Shaham, Tali Dekel, Tomer MichaeliAbstract:We introduce SinGAN, an unconditional Generative Model that can be learned from a single natural image. Our Model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.
Julien Jacques - One of the best experts on this subject based on the ideXlab platform.
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a Generative Model for rank data based on insertion sort algorithm
Computational Statistics & Data Analysis, 2013Co-Authors: Christophe Biernacki, Julien JacquesAbstract:An original and meaningful probabilistic Generative Model for full rank data Modelling is proposed. Rank data arise from a sorting mechanism which is generally unobservable for statisticians. Assuming that this process relies on paired comparisons, the insertion sort algorithm is known as being the best candidate in order to minimize the number of potential paired misclassifications for a moderate number of objects to be ordered. Combining this optimality argument with a Bernoulli event during a paired comparison step, a Model that possesses desirable theoretical properties, among which are unimodality, symmetry and identifiability is obtained. Maximum likelihood estimation can also be performed easily through an EM or a SEM-Gibbs algorithm (depending on the number of objects to be ordered) by involving the latent initial presentation order of the objects. Finally, the practical relevance of the proposal is illustrated through its adequacy with several real data sets and a comparison with a standard rank data Model.
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A Generative Model for rank data based on an insertion sorting algorithm
Computational Statistics and Data Analysis, 2013Co-Authors: Christophe Biernacki, Julien JacquesAbstract:An original and meaningful probabilistic Generative Model for full rank data Modelling is proposed. Rank data arise from a sorting mechanism which is generally unobservable for statisticians. Assuming that this process relies on paired comparisons, the insertion sort algorithm is known as being the best candidate in order to minimize the number of potential paired misclassifications for a moderate number of objects to be ordered. Combining this optimality argument with a Bernoulli event during paired comparison step, a Model that possesses desirable theoretical properties, among which are unimodality, symmetry and identifiability is obtained. Maximum likelihood estimation can also be performed easily through an EM or a SEM-Gibbs algorithm (depending on the number of objects to be ordered) by involving the latent initial presentation order of the objects. Finally, the practical relevance of the proposal is illustrated through its adequacy with several real data sets and a comparison with a standard rank data Model.
Tamar Rott Shaham - One of the best experts on this subject based on the ideXlab platform.
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SinGAN: Learning a Generative Model from a Single Natural Image
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Tamar Rott Shaham, Tali Dekel, Tomer MichaeliAbstract:We introduce SinGAN, an unconditional Generative Model that can be learned from a single natural image. Our Model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.
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ICCV - SinGAN: Learning a Generative Model From a Single Natural Image
2019 IEEE CVF International Conference on Computer Vision (ICCV), 2019Co-Authors: Tamar Rott Shaham, Tali Dekel, Tomer MichaeliAbstract:We introduce SinGAN, an unconditional Generative Model that can be learned from a single natural image. Our Model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.
