Protein-Ligand Affinity Prediction
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Benchmarks
Most implemented
BAPULM: Binding Affinity Prediction using Language Models
Papers
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 Prediction