The field of molecular generation and protein modeling is rapidly advancing, with a focus on developing innovative methods for generating accurate and diverse molecular structures. Recent developments have seen the integration of machine learning and deep learning techniques, such as diffusion models and transformer-based architectures, to improve the efficiency and effectiveness of molecular generation and protein modeling. These advancements have the potential to revolutionize fields such as drug discovery and materials science. Notably, papers such as ProtPainter and MetaMolGen have introduced novel approaches to protein backbone generation and molecular design, while works like IPBind and Hyformer have demonstrated the power of geometric deep learning and joint modeling for protein-ligand binding affinity prediction and molecule generation. Additionally, papers like OmegAMP and polyGen have showcased the potential of controlled diffusion and latent diffusion models for targeted antimicrobial peptide generation and atomic-level polymer structure generation, respectively.
Breakthroughs in Molecular Generation and Protein Modeling
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ProtPainter: Draw or Drag Protein via Topology-guided Diffusion
Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction
Interpretable Deep Learning for Polar Mechanistic Reaction Prediction
MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
Enhancing Reinforcement learning in 3-Dimensional Hydrophobic-Polar Protein Folding Model with Attention-based layers
Clifford Group Equivariant Diffusion Models for 3D Molecular Generation
SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures
Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning
Unified Molecule Generation and Property Prediction
Targeted AMP generation through controlled diffusion with efficient embeddings
Combining GCN Structural Learning with LLM Chemical Knowledge for or Enhanced Virtual Screening
polyGen: A Learning Framework for Atomic-level Polymer Structure Generation