Papers Protein-Ligand Affinity Prediction
“Protein-Ligand Affinity Prediction” 태그가 달린 논문 10편 · 필터 해제
PLANET v2.0: A comprehensive Protein-Ligand Affinity Prediction Model Based on Mixture Density Network
Drug discovery represents a time-consuming and financially intensive process, and virtual screening can accelerate it. Scoring functions, as one of the tools guiding virtual screening, have their precision closely tied t…
Protein-Ligand Affinity PredictionGraph Neural NetworkDrug DiscoveryTowards Precision Protein-Ligand Affinity Prediction Benchmark: A Complete and Modification-Aware DAVIS Dataset
Advancements in AI for science unlocks capabilities for critical drug discovery tasks such as protein-ligand binding affinity prediction. However, current models overfit to existing oversimplified datasets that does not …
Protein-Ligand Affinity PredictionDrug DiscoveryBAPULM: Binding Affinity Prediction using Language Models
Identifying drug-target interactions is essential for developing effective therapeutics. Binding affinity quantifies these interactions, and traditional approaches rely on computationally intensive 3D structural data. In…
Drug Discoverymolecular representationPredictionProtein-Ligand Affinity PredictionPLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers
Understanding protein-ligand binding affinity is crucial for drug discovery, enabling the identification of promising drug candidates efficiently. We introduce PLAPT, a novel model leveraging transfer learning from pre-t…
Drug DiscoveryPredictionProtein-Ligand Affinity PredictionTransfer LearningProtein-ligand binding representation learning from fine-grained interactions
The binding between proteins and ligands plays a crucial role in the realm of drug discovery. Previous deep learning approaches have shown promising results over traditional computationally intensive methods, but resulti…
Drug DiscoveryPredictionProtein-Ligand Affinity PredictionRepresentation Learning+1Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes
Recent advances in applying Graph Neural Networks (GNNs) to molecular science have showcased the power of learning three-dimensional (3D) structure representations with GNNs. However, most existing GNNs suffer from the l…
Graph Neural NetworkMolecular Property PredictionProtein-Ligand Affinity PredictionHigh Performance of Gradient Boosting in Binding Affinity Prediction
Prediction of protein-ligand (PL) binding affinity remains the key to drug discovery. Popular approaches in recent years involve graph neural networks (GNNs), which are used to learn the topology and geometry of PL compl…
Drug DiscoveryProtein-Ligand Affinity PredictionVocal Bursts Intensity PredictionStructure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning th…
Drug DiscoveryGraph AttentionGraph Neural NetworkProtein-Ligand Affinity PredictionA Point Cloud-Based Deep Learning Strategy for Protein-Ligand Binding Affinity Prediction
There is great interest to develop artificial intelligence-based protein-ligand affinity models due to their immense applications in drug discovery. In this paper, PointNet and PointTransformer, two pointwise multi-layer…
Drug DiscoveryProtein-Ligand Affinity PredictionResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning
Motivation: Protein-ligand affinity prediction is an important part of structure-based drug design. It includes molecular docking and affinity prediction. Although molecular dynamics can predict affinity with high accura…
Drug DesignMolecular DockingPredictionProtein-Ligand Affinity Prediction