arXiv:2606. 11651v1 Announce Type: new Abstract: Synthetic random heteropolymers (RHPs), consisting of a predefined set of monomers, offer an approach toward the design of protein-like materials.
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.
SimpleDesign is a single-stage, end-to-end model for joint protein sequence and structure design that eliminates the need for multi-stage training. It combines discrete cross-entropy for sequences with a regression objective for structures, using a Mixture-of-Transformer architecture to handle modality-specific processing while maintaining global self-attention. Trained on over 2 million sequence-structure pairs, SimpleDesign achieves strong performance on co-design and unconditional generation benchmarks.
arXiv:2504. 12075v4 Announce Type: replace Abstract: In the present work, a generative deep learning framework combining a Co-optimized Variational Autoencoder (Co-VAE) with quantitative structure-property relationship (QSPR) techniques is developed to enable inverse molecular design of fuels.
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
arXiv:2506. 07459v4 Announce Type: replace Abstract: Protein generative models have shown remarkable promise in protein design, yet their success rates remain constrained by reliance on curated sequence-structure datasets and by misalignment between supervised objectives and real design goals.
arXiv:2608. 16111v1 Announce Type: cross Abstract: Retrosynthesis is a cornerstone of drug discovery and organic synthesis.
The paper reports a large-scale, compute-controlled study of Chemical Language Models (CLMs) involving over 30,000 experiments across different molecular representations, tokenizations, model sizes, datasets, and architectures. It finds clear scaling trends in pretraining loss but shows that these improvements do not translate into proportional gains in goal-directed molecular design, with chemical syntax saturating early while semantic properties develop more slowly. The authors release a new suite of models, NovoMolGen, that achieves state-of-the-art results in drug discovery tasks, highlighting a disconnect between representation learning and downstream design and calling for new pretraining paradigms that target chemical semantics.
arXiv:2606. 10080v1 Announce Type: cross Abstract: Generative models have shown remarkable progress in a variety of domains such as protein design, but such power enables the opaque generation of hazardous proteins.
arXiv:2411. 04440v1 Announce Type: cross Abstract: Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive.
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.
arXiv:2606. 07567v1 Announce Type: cross Abstract: Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure.
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.