REVIEW 8 cited by
Generating 3D Molecules for Target Protein Binding
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and effective framework, known as GraphBP, to generate 3D molecules that bind to given proteins by placing atoms of specific types and locations to the given binding site one by one. In particular, at each step, we first employ a 3D graph neural network to obtain geometry-aware and chemically informative representations from the intermediate contextual information. Such context includes the given binding site and atoms placed in the previous steps. Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system. Finally, to place a new atom, we generate its atom type and relative location w.r.t. the constructed local coordinate system via a flow model. We also consider generating the variables of interest sequentially to capture the underlying dependencies among them. Experiments demonstrate that our GraphBP is effective to generate 3D molecules with binding ability to target protein binding sites. Our implementation is available at https://github.com/divelab/GraphBP.
Forward citations
Cited by 8 Pith papers
-
Closer through commonality: Enhancing hypergraph contrastive learning with shared groups
HyFi improves hypergraph contrastive learning by weighting nodes that share hyperedges as 'weak positives' and using feature noise instead of topology corruption for augmentation.
-
Exploring Discrete Flow Matching for 3D De Novo Molecule Generation
FlowMol-CTMC, using discrete-state continuous-time Markov chain flows, generates 3D molecules with higher stability and validity than prior 3D generation models while using far fewer parameters.
-
IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
IBEX trains a 3D diffusion model on scaffold-hopping tasks and refines generated poses with a six-degree-of-freedom physics optimization, raising zero-shot docking success from 53% to 64% on CBGBench.
-
Conditional Chemical Language Models are Versatile Tools in Drug Discovery
SAFE-T is a single conditional chemical language model that unifies scoring and generation of drug-like molecules from target family, protein, and mechanism-of-action prompts.
-
MolFORM: Multi-modal Flow Matching for Structure-Based Drug Design
A flow-matching model with direct preference optimization fine-tuning generates protein-binding molecules faster than diffusion baselines, with improved docking scores on the CrossDocked2020 benchmark.
-
Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion
READ couples contrastively aligned latent diffusion with pocket-similarity retrieval to generate 3D ligands, reporting Rank 1 on CBGBench and lower Vina energies than native ligands.
-
BoKDiff: Best-of-K Diffusion Alignment for Target-Specific 3D Molecule Generation
BoKDiff fine-tunes a diffusion model for 3D ligand generation on the highest-scoring candidates using QED, SA, and Vina rewards, and shows that best-of-N sampling alone improves property metrics.
-
Rectified Flow For Structure Based Drug Design
A rectified-flow generative model for structure-based drug design reports SOTA Vina Dock (-8.50) but its bond loss equation is constant in the model parameters and hyperparameters are tuned on the test set.
Discussion (0). Continue with ORCID to comment.