PairFuse is a lightweight, parameter‑efficient method that fuses pretrained all‑atom representations into a geometric latent diffusion model for unified binder design. By adding layerwise adapters, it enriches the diffusion model’s molecular block features and incorporates pairwise structural information into the denoiser. Experiments on antibody, peptide, and small‑molecule binder‑design benchmarks show that PairFuse outperforms both the base diffusion model and existing representation‑fusion baselines.
arXiv:2606. 25006v1 Announce Type: new Abstract: Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints.
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.
TorchCraft is a unified binder‑design framework that optimizes sequence logits using a frozen all‑atom structure predictor. It integrates confidence, contact, geometric, and sequence‑prior objectives within TorchFold to design minibinders, framework‑conditioned VHHs, cyclic peptides, and ligand‑binding proteins. Using pretrained AlphaFold 3 weights, TorchCraft produced experimentally validated binders across four targets without post‑hoc redesign, and computational tests confirmed its applicability to cyclic peptides and ligand‑conditioned pocket design.
arXiv:2607. 09998v1 Announce Type: new Abstract: Macrocyclic peptides are an increasingly important therapeutic modality, but existing computational methods for modeling their structures and properties are limited in scope and do not generalize well across the synthetically accessible chemical space.
arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.
arXiv:2607. 12380v1 Announce Type: new Abstract: Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its own graph, equivariant, or frame-based architecture.
arXiv:2606. 08375v1 Announce Type: new Abstract: All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems.
arXiv:2508.14748v2 Announce Type: replace-cross Abstract: The increasing variety of molecular data creates a need for generative models that can flexibly incorporate heterogeneous constraints across...
CODesign is a co-design framework that jointly generates protein sequences and structures to improve consistency between them. It introduces a large consistency‑distilled dataset of about 105,000 dimers and employs a multimodal joint flow model with a consistency‑aware resampling strategy to iteratively refine sequences and side chains. The approach achieves state‑of‑the‑art in silico success rates for protein‑ and ligand‑target binder design, with ablation studies showing a 70.9% performance boost from the distilled dataset and further gains from the resampling mechanism.
Mol-JEPA is a scalable multimodal framework that learns molecular world models by using modality masking instead of suboptimal perturbations. It incorporates diverse data such as molecular structures, cellular phenotypes, binding affinities, ADMET profiles, quantum chemistry simulations, and other drug‑discovery information. Benchmarks show that the representations it learns perform strongly, highlighting the benefit of embedding biochemical context via latent‑space prediction.
arXiv:2607. 01105v1 Announce Type: new Abstract: We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synthetic accessibility (how to make it).
FuseDiff is an end‑to‑end diffusion model designed for dual‑target structure‑based drug design, jointly generating a ligand graph and two pocket‑specific binding poses conditioned on both target pockets. It employs a message‑passing backbone with Dual‑target Local Context Fusion (DLCF) to fuse ligand atom contexts from both pockets, preserving symmetry while enabling expressive joint modeling. The model enforces topological consistency across the two poses and allows target‑specific geometric adaptation, achieving state‑of‑the‑art docking performance and enabling systematic assessment of dual‑target pose quality before docking‑based pose search.