SiDGen: Structure-informed Diffusion for Generative modeling of Ligands for Proteins

• URL: http://arxiv.org/abs/2511.09529v1
• Date: Thu, 13 Nov 2025 02:00:48 GMT
• Title: SiDGen: Structure-informed Diffusion for Generative modeling of Ligands for Proteins
• Authors: Samyak Sanghvi, Nishant Ranjan, Tarak Karmakar,
• Abstract summary: We present SiDGen, a protein-conditioned diffusion framework that integrates masked SMILES generation with lightweight folding-derived features for pocket awareness.<n>SiDGen supports two conditioning pathways: a streamlined mode that pools coarse structural signals from protein embeddings and a full mode that injects localized pairwise biases for stronger coupling.<n>In automated benchmarks, SiDGen generates with high validity, uniqueness, and novelty, while achieving competitive performance in docking-based evaluations and maintaining reasonable molecular properties.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Designing ligands that are both chemically valid and structurally compatible with protein binding pockets is a key bottleneck in computational drug discovery. Existing approaches either ignore structural context or rely on expensive, memory-intensive encoding that limits throughput and scalability. We present SiDGen (Structure-informed Diffusion Generator), a protein-conditioned diffusion framework that integrates masked SMILES generation with lightweight folding-derived features for pocket awareness. To balance expressivity with efficiency, SiDGen supports two conditioning pathways: a streamlined mode that pools coarse structural signals from protein embeddings and a full mode that injects localized pairwise biases for stronger coupling. A coarse-stride folding mechanism with nearest-neighbor upsampling alleviates the quadratic memory costs of pair tensors, enabling training on realistic sequence lengths. Learning stability is maintained through in-loop chemical validity checks and an invalidity penalty, while large-scale training efficiency is restored \textit{via} selective compilation, dataloader tuning, and gradient accumulation. In automated benchmarks, SiDGen generates ligands with high validity, uniqueness, and novelty, while achieving competitive performance in docking-based evaluations and maintaining reasonable molecular properties. These results demonstrate that SiDGen can deliver scalable, pocket-aware molecular design, providing a practical route to conditional generation for high-throughput drug discovery.

Related papers

• SaDiT: Efficient Protein Backbone Design via Latent Structural Tokenization and Diffusion Transformers [50.18388227899971]
  We present SaDiT, a novel framework that accelerates protein backbone generation by integrating SaProt Tokenization with a Diffusion Transformer (DiT) architecture.<n>Experiments demonstrate that SaDiT outperforms state-of-the-art models, including RFDiffusion and Proteina, in both computational speed and structural viability.
  arXiv  Detail & Related papers  (2026-02-06T13:50:13Z)
• Edge-aware GAT-based protein binding site prediction [3.3941174310007685]
  We propose an Edge-aware Graph Attention Network (Edge-aware GAT) model for the fine-grained prediction of binding sites across biomolecules.<n>Our method constructs atom-level graphs and integrates multidimensional structural features, including geometric descriptors.<n>Our model achieves an ROC-AUC of 0.93 for protein-protein binding site prediction, outperforming several state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-01-05T14:09:57Z)
• S$^2$Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening [72.89086338778098]
  We propose a two-stage framework for protein-ligand contrastive representation learning.<n>In the first stage, we perform protein sequence pretraining on ChemBL using an ESM2-based backbone.<n>In the second stage, we fine-tune on PDBBind by fusing sequence and structure information through a residue-level gating module.<n>This auxiliary task guides the model to accurately localize binding residues within the protein sequence and capture their 3D spatial arrangement.
  arXiv  Detail & Related papers  (2025-11-10T11:57:47Z)
• A Novel Framework for Multi-Modal Protein Representation Learning [13.33566214386641]
  We propose Diffused and Aligned Multi-modal Protein Embedding (DAMPE), a unified framework that addresses two core mechanisms.<n>First, we propose Optimal Transport (OT)-based representation alignment that establishes correspondence between intrinsic embedding spaces of different modalities.<n>Second, we develop a Conditional Graph Generation (CGG)-based information fusion method, where a condition encoder fuses the aligned intrinsic embeddings to provide informative cues for graph reconstruction.
  arXiv  Detail & Related papers  (2025-10-27T12:33:01Z)
• ProteinAE: Protein Diffusion Autoencoders for Structure Encoding [64.77182442408254]
  We introduce ProteinAE, a novel and streamlined protein diffusion autoencoder.<n>ProteinAE directly maps protein backbone coordinates from E(3) into a continuous, compact latent space.<n>We demonstrate that ProteinAE achieves state-of-the-art reconstruction quality, outperforming existing autoencoders.
  arXiv  Detail & Related papers  (2025-10-12T14:30:32Z)
• NIRVANA: Structured pruning reimagined for large language models compression [50.651730342011014]
  We introduce NIRVANA, a novel pruning method designed to balance immediate zero-shot preservation accuracy with robust fine-tuning.<n>To further address the unique challenges posed by structured pruning, NIRVANA incorporates an adaptive sparsity allocation mechanism across layers and modules.<n>Experiments conducted on Llama3, Qwen, T5 models demonstrate that NIRVANA outperforms existing structured pruning methods under equivalent sparsity constraints.
  arXiv  Detail & Related papers  (2025-09-17T17:59:00Z)
• ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions [64.17845687013434]
  We propose ReDiSC, a structured diffusion model for structured node classification.<n>We show that ReDiSC achieves superior or highly competitive performance compared to state-of-the-art GNN, label propagation, and diffusion-based baselines.<n> Notably, ReDiSC scales effectively to large-scale datasets on which previous structured diffusion methods fail due to computational constraints.
  arXiv  Detail & Related papers  (2025-07-19T04:46:53Z)
• Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion [22.204642926984526]
  READ is introduced, which is the first to merge molecular Retrieval-Augmented Generation with an SE(3)-equivariant diffusion model.<n>It can achieve very competitive performance in CBGBench, surpassing state-of-the-art generative models and even native scaffolds.
  arXiv  Detail & Related papers  (2025-06-17T13:09:11Z)
• Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data [0.0]
  Coarse-grained (CG) models provide an effective route to reducing the complexity of molecular simulations.<n>We introduce a fully data-free, generative framework for CG that directly targets the all-atom Boltzmann distribution.<n>We show that the method captures all relevant modes of the Boltzmann distribution, reconstructs atomic configurations, and automatically learns physically meaningful CG representations.
  arXiv  Detail & Related papers  (2025-04-29T17:05:27Z)
• Fast and Accurate Blind Flexible Docking [79.88520988144442]
  Molecular docking that predicts the bound structures of small molecules (ligands) to their protein targets plays a vital role in drug discovery.<n>We propose FABFlex, a fast and accurate regression-based multi-task learning model designed for realistic blind flexible docking scenarios.
  arXiv  Detail & Related papers  (2025-02-20T07:31:13Z)
• The Latent Road to Atoms: Backmapping Coarse-grained Protein Structures with Latent Diffusion [19.85659309869674]
  Latent Diffusion Backmapping (LDB) is a novel approach leveraging denoising diffusion within latent space to address these challenges. We evaluate LDB's state-of-the-art performance on three distinct protein datasets. Our results position LDB as a powerful and scalable approach for backmapping, effectively bridging the gap between CG simulations and atomic-level analyses in computational biology.
  arXiv  Detail & Related papers  (2024-10-17T06:38:07Z)
• Protein Design with Guided Discrete Diffusion [67.06148688398677]
  A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. We propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods.
  arXiv  Detail & Related papers  (2023-05-31T16:31:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.