Pre-training with fractional denoising to enhance molecular property prediction
Deep learning methods have been considered promising for accelerating molecular
screening in drug discovery and material design. Due to the limited availability of labelled …
screening in drug discovery and material design. Due to the limited availability of labelled …
Navigating chemical-linguistic sharing space with heterogeneous molecular encoding
Chemical language models (CLMs) are prominent for their effectiveness in exploring
chemical space and enabling molecular engineering. However, while exploring chemical …
chemical space and enabling molecular engineering. However, while exploring chemical …
Pretraining graph transformer for molecular representation with fusion of multimodal information
Molecular representation learning (MRL) is essential in certain applications including drug
discovery and life science. Despite advancements in multiview and multimodal learning in …
discovery and life science. Despite advancements in multiview and multimodal learning in …
Self-Supervised Molecular Representation Learning With Topology and Geometry
X Zang, J Zhang, B Tang - IEEE Journal of Biomedical and …, 2024 - ieeexplore.ieee.org
Molecular representation learning is of great importance for drug molecular analysis. The
development in molecular representation learning has demonstrated great promise through …
development in molecular representation learning has demonstrated great promise through …
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction
Chemical representation learning has gained increasing interest due to the limited
availability of supervised data in fields such as drug and materials design. This interest …
availability of supervised data in fields such as drug and materials design. This interest …
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild
In recent decades, chemistry publications and patents have increased rapidly. A significant
portion of key information is embedded in molecular structure figures, complicating large …
portion of key information is embedded in molecular structure figures, complicating large …
HiMolformer: Integrating graph and sequence representations for predicting liver microsome stability with SMILES
S Yun, G Nam, J Koo - Computational Biology and Chemistry, 2024 - Elsevier
In the initial stages of drug discovery or pre-clinical studies, understanding the metabolic
stability of new molecules is crucial. Recently, research on pre-trained deep learning for …
stability of new molecules is crucial. Recently, research on pre-trained deep learning for …
Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
Molecule-and-text cross-modal representation learning has emerged as a promising
direction for enhancing the quality of molecular representation, thereby improving …
direction for enhancing the quality of molecular representation, thereby improving …
Equivariant Pretrained Transformer for Unified Geometric Learning on Multi-Domain 3D Molecules
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in
various scientific applications. However, prior efforts typically focus on pretraining models on …
various scientific applications. However, prior efforts typically focus on pretraining models on …
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis
Molecular representation learning is pivotal for various molecular property prediction tasks
related to drug discovery. Robust and accurate benchmarks are essential for refining and …
related to drug discovery. Robust and accurate benchmarks are essential for refining and …