Scientists use AI to generate designs of potential new molecules, a process that is 10 times faster and more precise.
A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules
School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K. Kuano, Hauxton House, Mill Scitech Park, Mill Lane, Cambridge, England CB22 5HX, U.K. Department ...
Circular RNAs (circRNAs) are a unique class of non-coding RNAs with stable covalently closed structures that play key regulatory roles in gene expression and drug response. However, experimental ...
BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...
An app for segmentation and classification of images of cells from optical microscope. This project uses marker controlled watershed (openCv), and pretrained ResNet-50 model (tensorflow) a ...
Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...
The Nature Index 2025 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their output in ...
Determining drug-target affinity (DTA) is a pivotal step in drug discovery, where in silico methods can significantly improve efficiency and reduce costs. Artificial intelligence (AI), especially deep ...
A study published in the journal Stem Cell Reports on March 23, led by Dr. Ryosuke Tsuchimochi and Professor Jun Takahashi, examined the effects of combining cell transplantation and gene therapy for ...
Abstract: The concept of generating molecular structures with specific desirable characteristics underlies some of the crucial problems in drug discovery. In this paper, we present a model which ...
Abstract: As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecules. Most of ...
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