The field of molecular design and modeling is rapidly advancing, with a focus on developing innovative methods for generating high-quality molecular structures with desirable properties. Recent research has emphasized the importance of uncertainty-aware and multi-objective approaches, as well as the integration of machine learning and diffusion models. Notable developments include the use of reinforcement learning to guide the optimization of molecular diffusion models, and the introduction of anisotropic noise distributions to improve molecular force field modeling. These advances have the potential to significantly impact fields such as drug discovery and molecular engineering.

Noteworthy papers include: Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design, which proposes a framework for optimizing molecular diffusion models toward multiple property objectives. Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modeling, which introduces a novel denoising framework that outperforms prior isotropic and homoscedastic denoising models. DiSE: A diffusion probabilistic model for automatic structure elucidation of organic compounds, which presents an end-to-end diffusion-based generative model for automated structure elucidation.