Hugging Face Trending Papers June 23, 2026

Scalable Peptide Design via Memory-Efficient Equivariant Transformer

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Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints. Latent generative frameworks offer an effective route for this problem by compressing fine grained atomic structures into block level latent representations and performing conditional generation in a compact latent space.

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arXiv Machine Learning

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Scalable Peptide Design via Memory-Efficient Equivariant Transformer

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arXiv Machine Learning

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arXiv Machine Learning

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arXiv Machine Learning

Jul 2

SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles

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Related stories

Scalable Peptide Design via Memory-Efficient Equivariant Transformer

Folding, Reasoning, and Scaling with Open-source Drug Discovery Engine

Vilya-1: An all-atom foundation model for macrocycle structure prediction and design

RiboSphere: Learning Unified and Efficient Representations of RNA Structures

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SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles