Advances in Generative Modeling for Molecular Design

The field of molecular design is rapidly advancing with the development of innovative generative models. Recent research has focused on improving the efficiency and accuracy of these models, enabling the design of novel molecules with specific properties. A key direction in this field is the integration of machine learning techniques, such as diffusion models and variational autoencoders, with traditional molecular design methods. This has led to the development of powerful tools for generating molecules with desired characteristics, such as high affinity for specific targets or reduced structural vibrations. Notable papers in this area include PPDiff, which introduces a diffusion model for designing protein-binding proteins with high affinity, and Evolvable Conditional Diffusion, which presents a method for guiding the generative process using black-box, non-differentiable multi-physics models. Other noteworthy papers include Bures-Wasserstein Flow Matching for Graph Generation, which proposes a novel framework for graph generation that respects the underlying geometry of graphs, and Sampling 3D Molecular Conformers with Diffusion Transformers, which introduces a framework for adapting diffusion transformers to molecular conformer generation. These advancements have the potential to revolutionize the field of molecular design and enable the discovery of novel molecules with significant therapeutic potential.

Sources

PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design

Evolvable Conditional Diffusion

Bures-Wasserstein Flow Matching for Graph Generation

Structured and Informed Probabilistic Modeling with the Thermodynamic Kolmogorov-Arnold Model

Minimizing Structural Vibrations via Guided Flow Matching Design Optimization

Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation

Sampling 3D Molecular Conformers with Diffusion Transformers

Provable Maximum Entropy Manifold Exploration via Diffusion Models

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