Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides

• URL: http://arxiv.org/abs/2511.00209v1
• Date: Fri, 31 Oct 2025 19:11:41 GMT
• Title: Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides
• Authors: Yiquan Wang, Yahui Ma, Yuhan Chang, Jiayao Yan, Jialin Zhang, Minnuo Cai, Kai Wei,
• Abstract summary: Diffusion models have emerged as a leading framework in generative modeling.<n>This review provides a systematic comparison of their application in designing two principal therapeutic modalities: small molecules and therapeutic peptides.<n>We conclude that the full potential of diffusion models will be unlocked by bridging these modality-specific gaps and integrating them into automated, closed-loop Design-Build-Test-Learn platforms.
• Score: 6.436002724512122
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models have emerged as a leading framework in generative modeling, showing significant potential to accelerate and transform the traditionally slow and costly process of drug discovery. This review provides a systematic comparison of their application in designing two principal therapeutic modalities: small molecules and therapeutic peptides. We analyze how a unified framework of iterative denoising is adapted to the distinct molecular representations, chemical spaces, and design objectives of each modality. For small molecules, these models excel at structure-based design, generating novel, pocket-fitting ligands with desired physicochemical properties, yet face the critical hurdle of ensuring chemical synthesizability. Conversely, for therapeutic peptides, the focus shifts to generating functional sequences and designing de novo structures, where the primary challenges are achieving biological stability against proteolysis, ensuring proper folding, and minimizing immunogenicity. Despite these distinct challenges, both domains face shared hurdles: the need for more accurate scoring functions, the scarcity of high-quality experimental data, and the crucial requirement for experimental validation. We conclude that the full potential of diffusion models will be unlocked by bridging these modality-specific gaps and integrating them into automated, closed-loop Design-Build-Test-Learn (DBTL) platforms, thereby shifting the paradigm from chemical exploration to the targeted creation of novel therapeutics.

Related papers

• Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design [0.8749675983608171]
  We propose an uncertainty-aware Reinforcement Learning framework to guide the optimization of 3D molecular diffusion models.<n>Our results demonstrate the strong potential of RL-guided generative diffusion models for advancing automated molecular design.
  arXiv  Detail & Related papers  (2025-10-24T04:49:23Z)
• UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials [62.72989417755985]
  We present UniGenX, a unified generative model for function in natural systems.<n>UniGenX represents heterogeneous inputs as a mixed stream of symbolic and numeric tokens.<n>It achieves state-of-the-art or competitive performance for the function-aware generation across domains.
  arXiv  Detail & Related papers  (2025-03-09T16:43:07Z)
• NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models [6.75152379258166]
  We present NeuralPLexer3, a flow-based generative model that achieves state-of-the-art prediction accuracy on key biomolecular interaction types.<n> Examined through newly developed benchmarking strategies, NeuralPLexer3 excels in vital areas that are crucial to structure-based drug design.
  arXiv  Detail & Related papers  (2024-12-14T08:28:45Z)
• Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization [147.7899503829411]
  AliDiff is a novel framework to align pretrained target diffusion models with preferred functional properties. It can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score.
  arXiv  Detail & Related papers  (2024-07-01T06:10:29Z)
• AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design [16.946648071157618]
  We propose a diffusion-based fragment-wise autoregressive generation model for structure-based drug design (SBDD) We design a novel molecule assembly strategy named conformal motif that preserves the conformation of local structures of molecules first. We then encode the interaction of the protein-ligand complex with an SE(3)-equivariant convolutional network and generate molecules motif-by-motif with diffusion modeling.
  arXiv  Detail & Related papers  (2024-04-02T14:44:02Z)
• DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization [49.85944390503957]
  DecompOpt is a structure-based molecular optimization method based on a controllable and diffusion model. We show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines.
  arXiv  Detail & Related papers  (2024-03-07T02:53:40Z)
• Molecule Design by Latent Prompt Transformer [76.2112075557233]
  This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task. We propose a novel generative model comprising three components: (1) a latent vector with a learnable prior distribution; (2) a molecule generation model based on a causal Transformer, which uses the latent vector as a prompt; and (3) a property prediction model that predicts a molecule's target properties and/or constraint values using the latent prompt.
  arXiv  Detail & Related papers  (2024-02-27T03:33:23Z)
• DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design [62.68420322996345]
  Existing structured-based drug design methods treat all ligand atoms equally. We propose a new diffusion model, DecompDiff, with decomposed priors over arms and scaffold. Our approach achieves state-of-the-art performance in generating high-affinity molecules.
  arXiv  Detail & Related papers  (2024-02-26T05:21:21Z)
• Retrieval-based Controllable Molecule Generation [63.44583084888342]
  We propose a new retrieval-based framework for controllable molecule generation. We use a small set of molecules to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria. Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning.
  arXiv  Detail & Related papers  (2022-08-23T17:01:16Z)
• Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning [75.95376096628135]
  We propose a novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design. In this setup, the agent learns to navigate through the immense synthetically accessible chemical space. We describe how the end-to-end training in this study represents an important paradigm in radically expanding the synthesizable chemical space.
  arXiv  Detail & Related papers  (2020-04-26T21:40:03Z)
• The Synthesizability of Molecules Proposed by Generative Models [3.032184156362992]
  Discovery of functional molecules is an expensive and time-consuming process. One class of techniques of growing interest for early-stage drug discovery is de novo molecular generation and optimization. These techniques can suggest novel molecular structures intended to maximize a multi-objective function. However, the utility of these approaches is stymied by ignorance of synthesizability.
  arXiv  Detail & Related papers  (2020-02-17T15:41:28Z)

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.