Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

From the start of a synthetic chemist's training, experiments are conducted based on recipes
from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist …

Large language models (LLMs) have shown strong performance in tasks across domains
but struggle with chemistry-related problems. These models also lack access to external …

Abstract We present the Open Graph Benchmark (OGB), a diverse set of challenging and
realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine …

ADMETlab 3.0 is the second updated version of the web server that provides a
comprehensive and efficient platform for evaluating ADMET-related parameters as well as …

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays
multidrug resistance. Discovering new antibiotics against A. baumannii has proven …

The discovery of novel structural classes of antibiotics is urgently needed to address the
ongoing antibiotic resistance crisis,,,,,,,–. Deep learning approaches have aided in exploring …

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to
discover new antibiotics. To address this challenge, we trained a deep neural network …

Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …

Molecular machine learning bears promise for efficient molecular property prediction and
drug discovery. However, labelled molecule data can be expensive and time consuming to …