EDMolGPT generates drug-like molecules from low-resolution electron density point clouds of holo binding pockets and shows effectiveness across 101 biological targets.
arXiv preprint arXiv:2303.03543 , year=
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UNVERDICTED 6representative citing papers
LENSEs improves representation-conditioned molecule generation by jointly training a multi-level representation head, perceptual loss, and REPA alignment on pretrained encoders, yielding 97.28% validity and 98.51% stability on GEOM-DRUG.
Flow-Direct constructs a reusable non-parametric guidance field from the log-density ratio of base and target distributions using all accumulated reward samples for feedback-efficient guidance in flow models.
An SE(3)-equivariant transformer encodes 3D protein-ligand interactions via contrastive learning for zero-shot virtual screening, and these embeddings condition a multimodal chemical language model to autoregressively generate target-specific molecules with favorable predicted binding properties.
D-Flow applies multi-modality flow matching and a mirror-image data augmentation to generate D-peptides with 10.2% higher sequence identity and 24.31% top affinity on the PepMerge benchmark.
Hyformer jointly models molecule generation and property prediction via alternating attention and joint pre-training, showing synergistic gains in conditional sampling, OOD prediction, and a drug design case for antimicrobial peptides.
citing papers explorer
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From Holo Pockets to Electron Density: GPT-style Drug Design with Density
EDMolGPT generates drug-like molecules from low-resolution electron density point clouds of holo binding pockets and shows effectiveness across 101 biological targets.
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Toward Better Geometric Representations for Molecule Generative Models
LENSEs improves representation-conditioned molecule generation by jointly training a multi-level representation head, perceptual loss, and REPA alignment on pretrained encoders, yielding 97.28% validity and 98.51% stability on GEOM-DRUG.
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Flow-Direct: Feedback-Efficient and Reusable Guidance for Flow Models via Non-Parametric Guidance Field
Flow-Direct constructs a reusable non-parametric guidance field from the log-density ratio of base and target distributions using all accumulated reward samples for feedback-efficient guidance in flow models.
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Structure-guided molecular design with contrastive 3D protein-ligand learning
An SE(3)-equivariant transformer encodes 3D protein-ligand interactions via contrastive learning for zero-shot virtual screening, and these embeddings condition a multimodal chemical language model to autoregressively generate target-specific molecules with favorable predicted binding properties.
-
D-Flow: Multi-modality Flow Matching for D-peptide Design
D-Flow applies multi-modality flow matching and a mirror-image data augmentation to generate D-peptides with 10.2% higher sequence identity and 24.31% top affinity on the PepMerge benchmark.
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Synergistic Benefits of Joint Molecule Generation and Property Prediction
Hyformer jointly models molecule generation and property prediction via alternating attention and joint pre-training, showing synergistic gains in conditional sampling, OOD prediction, and a drug design case for antimicrobial peptides.