Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization.

The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines. However, other crucial stages of these pipelines such as dataset selection, feature engineering, and model optimization for deployment have received less attention. Improving efficiency of end-to-end AI pipelines requires metadata of past executions of AI pipelines and all their stages.

Key Anodic Interfacial Phenomena and their Control in Next-Generation Lithium and Sodium Metal Batteries.

Advancing next-generation battery technologies requires a thorough understanding of the intricate phenomena occurring at anodic interfaces. This focused review explores key interfacial processes, examining their thermodynamics and consequences in ion transport and charge transfer kinetics. It begins with a discussion on the formation of the electro chemical double layer, based on the GuoyChapman model, and explores how charge carriers achieve equilibrium at the interface.

The q-RASPR approach for predicting the property and fate of persistent organic pollutants.

This study presents a quantitative read-across structure-property relationship (q-RASPR) approach that integrates the chemical similarity information used in read-across with traditional quantitative structure-property relationship (QSPR) models. This novel framework is applied to predict the physicochemical properties and environmental behaviors of persistent organic pollutants, specifically polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). By utilizing a curated dataset and incorporating similarity-based descriptors, the q-RASPR approach improves the accuracy of predictions, particularly for compounds with limited experimental data.

Small Molecule with Big Impact: Metarrestin Targets the Perinucleolar Compartment in Cancer Metastasis.

Metarrestin (ML246) is a first-in-class pyrrole-pyrimidine-derived small molecule that selectively targets the perinucleolar compartment (PNC). PNC is a distinct subnuclear structure predominantly found in solid tumor cells. The occurrence of PNC demonstrates a positive correlation with malignancy, serving as an indicator of tumor aggressiveness, progression, and metastasis.

Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.

We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally active drugs. We initially developed ML models using nine different algorithms separately on topological descriptors (referred to as simply "descriptors" in the subsequent sections of the manuscript) and MACCS fingerprints (referred to as "fingerprints" in the subsequent sections of the manuscript), thus generating 18 different ML QSAR models. Using the chemical spaces defined by the modeling descriptors and fingerprints, the similarity and error-based RASAR descriptors were computed, and the most discriminating RASAR descriptors were used to develop another set of 18 different ML c-RASAR models.