27 papers · ranked by Valyu relevance
Zineb Sordo, Eric Chagnon, Zixi Hu, Jeffrey J. Donatelli + 6 more
'Peter Andeer' 'Peter S. Nico' 'Trent Northen' 'Daniela Ushizima' 'Raimondo Schettini' 'Guanghui (Richard) Wang'] Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive…
Ambuj Tewari
Beginning with text and images, generative AI has expanded to audio, video, computer code, and molecules. Yet, if generative AI is the answer, what is the question? We explore the foundations of generation as a distinct machine learning task with connections to prediction, compression, and decision-making. We survey…
Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang
Diffusion models, a powerful and universal generative artificial intelligence technology, have achieved tremendous success and opened up new possibilities in diverse applications. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples…
Qiuhua Yi, Xiangfan Chen, Chenwei Zhang, Zehai Zhou + 3 more
'Xiangjie Kong' 'Arkaitz Zubiaga'] Diffusion models are a kind of math-based model that were first applied to image generation. Recently, they have drawn wide interest in natural language generation (NLG), a sub-field of natural language processing (NLP), due to their capability to generate varied and high-quality text…
Hao Zhang, Yang Liu, Xiaoyan Liu, Cheng Wang + 1 more
Background Molecular biology is crucial for drug discovery, protein design, and human health. Due to the vastness of the drug-like chemical space, depending on biomedical experts to manually design molecules is exceedingly expensive. Utilizing generative methods with deep learning technology offers an effective…
Yuansong Zhu, Yu Zhao
—Diffusion models have become a powerful family of deep generative models, with record-breaking performance in many applications. This paper first gives an overview and derivation of the basic theory of diffusion models, then reviews the research results of diffusion models in the field of natural language processing…
Alice Lacan, Romain André, Michele Sebag, Blaise Hanczar
RNA-seq data is used for precision medicine (e.g., cancer predictions), which benefits from deep learning approaches to analyze complex gene expression data. However, transcriptomics datasets often have few samples compared to deep learning standards. Synthetic data generation is thus being explored to address this…
Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen + 3 more
'Dong Xu' 'Jianlin Cheng'] Denoising diffusion models have emerged as one of the most powerful generative models in recent years. They have achieved remarkable success in many fields, such as computer vision, natural language processing (NLP), and bioinformatics. Although there are a few excellent reviews on diffusion…
Meenu Ajith, Vince D. Calhoun
The development of diffusion models, such as Glide, DALLE 2, Imagen, and Stable Diffusion, marks a significant advancement in generative AI for image synthesis. In this paper, we introduce a novel framework for synthesizing intrinsic connectivity networks (ICNs) by utilizing the nonlinear capabilities of denoising…
Hanqun Cao, Cheng Tan, Zhangyang Gao, Guangyong Chen + 2 more
'Pheng‐Ann Heng' 'Stan Z. Li'] Abstract—Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one…
Alice Lacan, Romain André, Michèle Sebag, Blaise Hanczar
Background RNA-seq data is used for precision medicine (e.g., cancer predictions), which benefits from deep learning approaches to analyze complex gene expression data. However, transcriptomics datasets often have few samples compared to deep learning standards. Synthetic data generation is thus being explored to…
Gaurav Raut, Apoorv Singh
Generative AI models have revolutionized various fields by enabling the creation of realistic and diverse data samples. Among these models, diffusion models have emerged as a powerful approach for generating high-quality images, text, and audio. This survey paper provides a comprehensive overview of generative AI…
Yan Liu, Tao Jiang, Rui Li, Lingling Yuan + 3 more
'Chen Li' 'Xiaoyan Li'] Diffusion models, a class of deep learning models based on probabilistic generative processes, progressively transform data into noise and then reconstruct the original data through an inverse process. Recently, diffusion models have gained attention in microscopic image analysis for their…
Sudeep Sarma, Harrison Truscott, Da Xu, Kendall Reid + 3 more
Diffusion models have emerged as the state-of-the-art method in generative artificial intelligence (AI) and have shown great success in image synthesis, video generation, molecular design, and protein structure prediction. For biophysical problems, such as protein folding and association, a fundamental question in…
Anwaar Ulhaq, Naveed Akhtar, Ganna Pogrebna
Diffusion Models (DMs) have demonstrated state-of-the-art performance in content generation without requiring adversarial training. These models are trained using a two-step process. First, a forward - diffusion - process gradually adds noise to a datum (usually an image). Then, a backward - reverse diffusion - process…
Michael Alverson, Sterling Baird, Ryan Murdock, Taylor Sparks
The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced machine learning methods for predicting entirely new crystal…
Yihuan Tian, Tao Yu, Zuling Cheng, Sunjung Lee + 1 more
To promote the inheritance of traditional culture, a variety of emerging methods rooted in machine learning and deep learning have been introduced. Dunhuang patterns, an important part of traditional Chinese culture, are difficult to collect in large numbers due to their limited availability. However, existing…
Haotian Teng, Ran Wang, Yihang Shen, Ye Yuan + 1 more
In computational chemistry, molecular docking—predicting the binding structure of a small molecule ligand to a protein—is vital for understanding interactions between small molecules and their protein targets, with broad applications in drug discovery (21). Traditional docking methods rely on energy-based scoring…
Junkil Park, Aseem Partap Singh Gill, Seyed Mohamad Moosavi, JIHAN KIM
The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E, Midjourney and Stable Diffusion, which are tools used for text-to-image generations. Mapping this workflow onto materials discovery, a new diffusion model was developed for the generation of pure…
Jie Lin, Mingyuan Xu, Hongming Chen
Shape-based virtual screening is a widely utilized method in ligand-based de novo drug design, aiming to identify molecules in chemical libraries that share similar 3D shapes but simultaneously possess novel 2D chemical structures compared to the reference compound. As an emerging technology, generative model is an…
Oleksandr Cherednichenko, Maria Poptsova
Deep learning methods have been successfully applied to the tasks of predicting non-B DNA structures, however model performance depends on the availability of experimental data for training. Experimental technologies for non-B DNA structure detection are limited to the subsets that are active at the time of an…
Mariana Vargas Vieyra, Pierre Ménard
We present a novel, alternative framework for learning generative models with goal-conditioned reinforcement learning. We define two agents, a goal conditioned agent (GC-agent) and a supervised agent (S-agent). Given a user-input initial state, the GC-agent learns to reconstruct the training set. In this context…
Juan Viguera Diez, Sara Romeo Atance, Ola Engkvist, Simon Olsson
The accurate prediction of thermodynamic properties is crucial in various fields such as drug discovery and materials design. This task relies on sampling from the underlying Boltzmann distribution, which is challenging using conventional approaches such as simulations. In this work, we introduce Surrogate…
Benjamin Kuznets-Speck, Jaekwon Jung, Pornchanan Pholraksa, Adrianne Zhong + 4 more
Classification and regression are cornerstones of computational biology and science at large, from identifying cell types to stratifying patients by disease state. Current deep learning classifiers provide accurate predictions but offer neither uncertainty estimates nor insight into which features matter most. On the…
Authors not listed
Fragment-based drug design (FBDD) has become a key approach in structure-based drug discovery, allowing researchers to systematically develop molecular fragments into potent ligands. Although recent generative AI models, such as diffusion-based approaches, show great potential for designing new molecules, applying them…
Nathan Mancheun Lui, Max D Li, Matthew Ford
Deep generative models for molecular graphs offer a new avenue for property optimization in drug discovery. Optimizing differentiable models that generate molecular graphs is certainly faster, cheaper, and much more accessible than traditional methods of chemical synthesis. Recent advances in generative modeling have…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…