arXiv:2608. 19808v1 Announce Type: new Abstract: Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability.
arXiv:2606. 12991v1 Announce Type: new Abstract: Cyclic peptides represent a promising class of therapeutic compounds in modern drug discovery, often offering improved stability and binding affinity.
PepEVOLVE is a dynamic, position‑aware framework for optimizing macrocyclic peptides that learns where to edit and how to improve multiple properties. It enhances pretraining with dynamic masking and CHUCKLES shifting, employs a multi‑armed‑bandit router to identify high‑reward residues, and uses an evolving optimization algorithm with group‑relative advantage to stabilize updates. In in‑silico tests on a Rev‑binding macrocycle benchmark, PepEVOLVE surpassed PepINVENT, achieving higher permeability‑weighted scores and converging faster while respecting structural constraints.
arXiv:2602. 11189v2 Announce Type: replace-cross Abstract: Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties.
Pepti-drift is a one‑step peptide generation framework that uses a single latent refinement and parallel decoding, avoiding costly inference‑time guidance. It incorporates attraction toward target‑specific binders and repulsion from liability‑associated regions, achieving an 18.37% predicted Safe‑Active yield across 88 held‑out targets. The method delivers competitive gains to multi‑property‑guided baselines while reducing generation cost by 468×, enabling scalable and fair high‑throughput peptide design.
arXiv:2606. 14510v1 Announce Type: new Abstract: Macrocyclic peptides are promising therapeutic candidates for intracellular targets, but their design requires simultaneous control over non-natural monomer chemistry, ring topology, membrane permeability, and target binding.
arXiv:2608. 11483v1 Announce Type: new Abstract: Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints.
arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implement...
arXiv:2610.07560v1 Announce Type: new Abstract: Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, lim...
PocketVE is a protein-pocket-conditioned variance‑exploding diffusion framework that integrates stable 3D coordinate denoising, classifier‑free property guidance, and adaptive protein perturbation. It improves 3D validity from 58.6% to 80.6% and reduces strain energy from 457.4 to 127.9 on CrossDocked2020 while maintaining competitive docking and property scores. The study shows moderate guidance balances target objectives with geometric quality, and diagnostics confirm enhanced pocket compatibility.
arXiv:2605. 25681v2 Announce Type: replace-cross Abstract: Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard single-target generation because one candidate must satisfy two binding requirements while preserving drug-likeness and synthesizability.
The paper introduces MODS, a modular framework that performs multi‑objective protein optimization by steering diffusion models at inference time. MODS samples Chebyshev scalarization weights and a surrogate ensemble member to create a scalar steering reward, allowing the generation of sequences that balance multiple properties. After each batch of experimental evaluations, MODS updates its surrogates without retraining the generative model, and it achieves competitive hypervolume results across six protein optimization tasks, outperforming baselines on several.