The expansion of the chemical space to tangible libraries containing billions of
synthesizable molecules opens exciting opportunities for drug discovery, but also …

Abstract Machine learning (ML) has been rapidly transforming the landscape of natural
sciences and has the potential to revolutionize the process of data analysis and hypothesis …

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 …

Deep graph neural networks are extensively utilized to predict chemical reactivity and
molecular properties. However, because of the complexity of chemical space, such models …

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 …

Reaction optimization and characterization depend on reliable measures of reaction yield,
often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC …

Ionizable lipids are a key component of lipid nanoparticles, the leading nonviral messenger
RNA delivery technology. Here, to advance the identification of ionizable lipids beyond …

Accurate prediction of thermodynamic solubility by machine learning remains a challenge.
Recent models often display good performances, but their reliability may be deceiving when …

Buried-interface engineering is crucial to the performance of perovskite solar cells. Self-
assembled monolayers and buffer layers at the buried interface can optimize charge transfer …

The utilization of electrolyte additives has been regarded as an efficient strategy to construct
dendrite-free aqueous zinc-ion batteries (AZIBs). However, the blurry screening criteria and …