Geometric diffusion model for molecular generation

Significant progress has been made in the field of deep generative models, with notable examples such as ChatGPT and image-generation tools like Midjourney, Stable Diffusion, and DALL·E showcasing remarkable performance. One of the key mathematical models behind these advancements is denoising diffu...

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Main Author: Mou, Bingyan
Other Authors: Xia Kelin
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/175669
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1756692024-05-06T15:36:21Z Geometric diffusion model for molecular generation Mou, Bingyan Xia Kelin School of Physical and Mathematical Sciences xiakelin@ntu.edu.sg Mathematical Sciences Graph neural networks Diffusion models Molecular generation Drug discovery Significant progress has been made in the field of deep generative models, with notable examples such as ChatGPT and image-generation tools like Midjourney, Stable Diffusion, and DALL·E showcasing remarkable performance. One of the key mathematical models behind these advancements is denoising diffusion models (DDMs, Ho et al., 2020; Song et al., 2021), which belong to a class of generative models aiming to reconstruct clean data from noisy observations. DDMs have demonstrated significant effectiveness in diverse applications, including image restoration, image inpainting, and generative modelling. In this project, our focus will be on a systematic study of denoising diffusion models and their integration with geometric representations for molecular generation. In general, DDMs operate by iteratively transforming noisy observed data towards a desired clean state through a series of diffusion steps. Each diffusion step involves updating the data using a diffusion process that gradually reduces the noise level. This process is typically guided by a learnable diffusion model that determines the transition probabilities for transforming the data at each step. Additionally, geometric models offer a more intrinsic and informative representation for molecules. By combining geometric representations with DDMs, we can explore the generation of various types of molecules. Overall, this project aims to investigate denoising diffusion models comprehensively and explore their synergy with geometric representations in the context of molecular generation. Bachelor's degree 2024-05-03T06:39:02Z 2024-05-03T06:39:02Z 2024 Final Year Project (FYP) Mou, B. (2024). Geometric diffusion model for molecular generation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175669 https://hdl.handle.net/10356/175669 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Mathematical Sciences
Graph neural networks
Diffusion models
Molecular generation
Drug discovery
spellingShingle Mathematical Sciences
Graph neural networks
Diffusion models
Molecular generation
Drug discovery
Mou, Bingyan
Geometric diffusion model for molecular generation
description Significant progress has been made in the field of deep generative models, with notable examples such as ChatGPT and image-generation tools like Midjourney, Stable Diffusion, and DALL·E showcasing remarkable performance. One of the key mathematical models behind these advancements is denoising diffusion models (DDMs, Ho et al., 2020; Song et al., 2021), which belong to a class of generative models aiming to reconstruct clean data from noisy observations. DDMs have demonstrated significant effectiveness in diverse applications, including image restoration, image inpainting, and generative modelling. In this project, our focus will be on a systematic study of denoising diffusion models and their integration with geometric representations for molecular generation. In general, DDMs operate by iteratively transforming noisy observed data towards a desired clean state through a series of diffusion steps. Each diffusion step involves updating the data using a diffusion process that gradually reduces the noise level. This process is typically guided by a learnable diffusion model that determines the transition probabilities for transforming the data at each step. Additionally, geometric models offer a more intrinsic and informative representation for molecules. By combining geometric representations with DDMs, we can explore the generation of various types of molecules. Overall, this project aims to investigate denoising diffusion models comprehensively and explore their synergy with geometric representations in the context of molecular generation.
author2 Xia Kelin
author_facet Xia Kelin
Mou, Bingyan
format Final Year Project
author Mou, Bingyan
author_sort Mou, Bingyan
title Geometric diffusion model for molecular generation
title_short Geometric diffusion model for molecular generation
title_full Geometric diffusion model for molecular generation
title_fullStr Geometric diffusion model for molecular generation
title_full_unstemmed Geometric diffusion model for molecular generation
title_sort geometric diffusion model for molecular generation
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175669
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