Framework can investigate regions of chemical space that are normally inaccessible, painting a clearer picture of how molecules can form, transform and interconvert ...

Scientists usually study the molecular machinery that controls gene expression from the perspective of a linear, two-dimensional genome—even though DNA and its bound proteins function in three ...

The earliest stage of drug discovery is governed by a simple constraint: there are far more possible drug-like molecules than any pharmaceutical laboratory could ever test. A new deep learning system, ...

We introduce MoltiTox, a novel multimodal fusion model for molecular toxicity prediction, designed to overcome the limitations of single-modality approaches in drug discovery. MoltiTox integrates four ...

Multi-View Conditional Information Bottleneck (MVCIB) is a novel architecture for pre-training Graph Neural Networks on 2D and 3D molecular structures and developed by NS Lab, CUK based on pure ...

The use of single-modal molecular representations limits the accuracy of standard Quantitative Structure–Property Relationship (QSPR) models, which are essential for speeding up drug discovery and ...

Abstract: Molecular property prediction holds significant importance in the fields of cheminformatics and drug discovery. Current modeling paradigms used for molecular representation mainly rely on 1D ...

Background: Molecular interactions are central to numerous challenges in chemistry and the life sciences. Whether in solute–solvent dissolution, adverse drug–drug interactions, or protein complex ...

Accurate molecular property prediction is fundamental to modern drug discovery and materials design. However, prevailing computational methods are often insufficient, as they rely on ...