Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with the increasing cost and limited generalization of...
arXiv:2606. 30170v1 Announce Type: cross Abstract: Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets.
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.
The paper presents a closed‑loop molecule generation pipeline that iteratively retrains on new quantum‑chemical simulation data, overcoming limitations of static generative models. This approach produces molecules whose properties extend up to 0.44 standard deviations beyond the training set and improves out‑of‑distribution classification accuracy by 79%. By conditioning on thermodynamic stability during the loop, the method yields a 3.5‑fold increase in the proportion of stable, potentially synthesizable molecules.
arXiv:2606. 17077v1 Announce Type: cross Abstract: Proton dissociation constants (pKa) are critical for functional molecule discovery and molecular modeling.
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.
The paper introduces MEVO, a generative molecular evolutionary framework that operates within a synthesizable chemical space for structure‑based drug design. MEVO uses a reaction‑aware generative model trained on reaction templates applied to Enamine Building Blocks, jointly producing molecules and synthesis sketches. The framework iteratively refines these molecules using a pocket‑aware evolutionary strategy guided by a hierarchical scoring function that combines fast approximations with rigorous Absolute Binding Free Energy Perturbation calculations, enabling the design of high‑affinity, synthetically feasible binders for diverse protein targets such as KRAS$^{ extrm{G12D}}$.
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...
arXiv:2606. 23856v1 Announce Type: new Abstract: Generative molecular models for drug design are a promising direction with much active research.
arXiv:2607. 20551v1 Announce Type: cross Abstract: Effective molecular representation learning is crucial for accurate molecular property prediction.
The paper introduces a latent style-based quantum Wasserstein GAN (QGAN) for drug design, integrating a variational autoencoder with a hybrid quantum GAN to generate molecular structures. The QGAN employs style-based noise reuploading and a Wasserstein gradient penalty to enhance trainability and reduce mode collapse, achieving comparable performance to classical models while using far fewer trainable parameters. Experiments on noiseless simulators and an IBM Heron quantum computer demonstrate the model’s capability to sample thousands of new molecules and reveal quantum noise effects.
arXiv:2607. 19816v1 Announce Type: cross Abstract: Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules.