25 papers · ranked by Valyu relevance
Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi + 7 more
'Chen Zhou' 'Chen Shao' 'Houssam Metni' 'Clint van Hoesel' 'Henrik Schopmans' 'Timo Sommer' 'Pascal Friederich'] Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict…
Yu Wang, Ryan A. Rossi, Namyong Park, Huiyuan Chen + 5 more
'Nesreen K. Ahmed' 'Puja Trivedi' 'Franck Dernoncourt' 'Danai Koutra' 'Tyler Derr'] Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous domains. This training paradigm over…
Shubham Gupta, Srikanta Bedathur
Temporal graphs represent the dynamic relationships among entities and occur in many real life application like social networks, e-commerce, communication, road networks, biological systems, and many more. They necessitate research beyond the work related to static graphs in terms of their generative modeling and…
Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu + 3 more
'Qiang Liu' 'Shu Wu'] Graphs are ubiquitous in encoding relational information of real-world objects in many domains. Graph generation, whose purpose is to generate new graphs from a distribution similar to the observed graphs, has received increasing attention thanks to the recent advances of deep learning models. In…
Han Huang, Leilei Sun, Bowen Du, Yanjie Fu + 1 more
—Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due to the discrete and high-dimensional nature of graphs, as well as permutation invariance to node orderings in underlying graph…
Faezeh Faez, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah, Hamid R. Rabiee + 1 more
'Hamid R. Rabiee' 'Sathishkumar V E'] Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems. Making these methods able to consider different conditions during the generation procedure even increases their…
Chisato Kumada, Tomoyuki Hiroyasu, Satoru Hiwa
Structural connectivity (SC) data are crucial for brain network analysis, but SC-based machine learning often suffers from limited data availability, hindering model generalization and robustness. Although data augmentation using deep generative models has attracted increasing attention, it remains unclear how…
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…
Yilin He, Xinyang Liu, Bo Chen, Mingyuan Zhou
Diffusion models have demonstrated effectiveness in generating natural images and have been extended to generate diverse data types, including graphs. This new generation of diffusion-based graph generative models has demonstrated significant performance improvements over methods that rely on variational autoencoders…
Iakovos Evdaimon, Giannis Nikolentzos, Michail Chatzianastasis, Hadi Abdine + 1 more
Latent Diffusion Models Authors: ['Iakovos Evdaimon' 'Giannis Nikolentzos' 'Michail Chatzianastasis' 'Hadi Abdine' 'Michalis Vazirgiannis'] Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties. Existing methods…
Ishaan Batta, Meenu Ajith, Vince D. Calhoun
In studying the brain’s functional connectivity and its associations with clinically observed assessments, novel learning frameworks modeling its network properties in conjunction with assessment variables are crucial to uncover variable-specific patterns via meaningful encoding and reconstruction. We present a…
Hiroaki Iwata, Taichi Nakai, Takuto Koyama, Shigeyuki Mtsumoto + 2 more
Molecular generation is crucial for advancing drug discovery, material design, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques, such as molecular…
Authors not listed
Deep generative models are transforming early-stage drug discovery, yet most current approaches are not well suited for realistic, small-data settings and often rely on simplified molecular representations such as linear strings, overlooking the inherent graph-based structure of molecules. To address this, we first…
Malte Rørmose Damgaard, Rasmus Pedersen, Thomas Bak
Title: Summary Inspired by the “cognitive hourglass” model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by…
Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang + 1 more
Discovering new structures in the chemical space is a long-standing challenge and has important applications to various fields such as chemistry, material science, and drug discovery. Deep generative models have been used in de novo molecule design to embed molecules in a meaningful latent space and then sample new…
Ran He, Jie Cao, Tieniu Tan
Generative artificial intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes while providing insights that might not be possible with traditional tools. To stimulate future research, this work provides a brief summary of the ongoing and…
Manfred Jaeger
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields…
Yutong Li, Pedro Henrique da Costa Avelar, Xinyue Chen, Li Zhang + 2 more
Fragment-based drug design (FBDD), where fragments act as starting points for molecular generation, is an effective way to constrain chemical space and improve generation for biologically active molecules. Key challenges in this process is to navigate through the vast molecular space, and produce promising molecules.…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli + 2 more
'Pietro Pinoli' 'Chandan K. Sen' 'George Truskey'] The advancement of computational genomics has significantly enhanced the use of data-driven solutions in disease prediction and precision medicine. Yet, challenges such as data scarcity, privacy constraints, and biases persist. Synthetic data generation offers a…
Conghao Wang, Yuguang Mu, Jagath C. Rajapakse
De novo design of bioactive drug molecules with potential to treat desired biological targets is a profound task in the drug discovery process. Existing approaches tend to leverage the pocket structure of the target protein to condition the molecule generation. However, even the pocket area of the target protein may…
Riccardo Smeriglio, Joana Rosell-Mirmi, Petia Radeva, Jordi Abante
Current genotype-to-phenotype models, such as poly-genic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the…
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…
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…
Andrés Martínez Mora, Dimitris Polychronopoulos, Michaël Ughetto, Sebastian Nilsson
Machine learning applications for the drug discovery pipeline have exponentially increased in the last few years. An example of these applications is the biological Knowledge Graph. These graphs represent biological entities and the relations between them based on existing knowledge. Graph machine learning models such…