Search Results for author:
Found 9 papers, 7 papers with code
Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors
Specifically, we propose a \emph{label aggregation} with \emph{pair-wise retrieval} and a \emph{representation aggregation} with \emph{point-wise retrieval} of the nearest neighbors.
3D-MolT5: Towards Unified 3D Molecule-Text Modeling with 3D Molecular Tokenization
This 3D structure token vocabulary enables the seamless combination of 1D sequence and 3D structure representations in a tokenized format, allowing 3D-MolT5 to encode molecular sequence (SELFIES), molecular structure, and text sequences within a unified architecture.
FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation
Molecular docking is a pivotal process in drug discovery.
Ranked #1 on
Blind Docking
on PDBbind
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology.
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual representations (e. g., IUPAC).
Ranked #1 on
Molecule Captioning
on ChEBI-20
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery.
Ranked #2 on
Text-based de novo Molecule Generation
on ChEBI-20
FABind: Fast and Accurate Protein-Ligand Binding
In this work, we propose $\mathbf{FABind}$, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding.
Ranked #4 on
Blind Docking
on PDBBind
Examining User-Friendly and Open-Sourced Large GPT Models: A Survey on Language, Multimodal, and Scientific GPT Models
Generative pre-trained transformer (GPT) models have revolutionized the field of natural language processing (NLP) with remarkable performance in various tasks and also extend their power to multimodal domains.
SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction
Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities.
Ranked #1 on
Drug Discovery
on KIBA
