Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion

• URL: http://arxiv.org/abs/2403.12987v1
• Date: Mon, 4 Mar 2024 07:40:25 GMT
• Title: Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
• Authors: Bowen Gao, Minsi Ren, Yuyan Ni, Yanwen Huang, Bo Qiang, Zhi-Ming Ma, Wei-Ying Ma, Yanyan Lan,
• Abstract summary: We introduce the Delta Score, a new metric for evaluating the specificity of molecular binding. We develop an innovative energy-guided approach using contrastive learning, with active compounds as decoys, to direct generative models toward creating molecules with high specificity.
• Score: 18.269722389716165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular generative methods and docking scores both have lacked consideration in terms of specificity, which means that generated molecules bind to almost every protein pocket with high affinity. To address this, we introduce the Delta Score, a new metric for evaluating the specificity of molecular binding. To further incorporate this insight for generation, we develop an innovative energy-guided approach using contrastive learning, with active compounds as decoys, to direct generative models toward creating molecules with high specificity. Our empirical results show that this method not only enhances the delta score but also maintains or improves traditional docking scores, successfully bridging the gap between SBDD and real-world needs.

Related papers

• Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design [81.95343363178662]
  atoms must maintain a minimum pairwise distance to avoid separation violations. NucleusDiff models the interactions between atomic nuclei and their surrounding electron clouds by enforcing the distance constraint. It reduces violation rate by up to 1000% and enhances binding affinity by up to 22.16%, surpassing state-of-the-art models for structure-based drug design.
  arXiv  Detail & Related papers  (2024-09-16T08:42:46Z)
• A Score-Based Density Formula, with Applications in Diffusion Generative Models [6.76974373198208]
  Score-based generative models (SGMs) have revolutionized the field of generative modeling, achieving unprecedented success in generating realistic and diverse content. Despite empirical advances, the theoretical basis for why optimizing the evidence lower bound (ELBO) on the log-likelihood is effective for training diffusion generative models, such as DDPMs, remains largely unexplored.
  arXiv  Detail & Related papers  (2024-08-29T17:59:07Z)
• 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)
• From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics [21.78568415483299]
  The reliability of the Vina docking score is increasingly questioned due to its susceptibility to overfitting. We propose a comprehensive evaluation framework that includes assessing the similarity of generated molecules to known active compounds. Our proposed metrics and dataset aim to bridge this gap, enhancing the practical applicability of future SBDD models.
  arXiv  Detail & Related papers  (2024-06-13T10:23:52Z)
• TAGMol: Target-Aware Gradient-guided Molecule Generation [19.977071499171903]
  3D generative models have shown significant promise in structure-based drug design (SBDD) We decouple the problem into molecular generation and property prediction. The latter synergistically guides the diffusion sampling process, facilitating guided diffusion and resulting in the creation of meaningful molecules with the desired properties. We call this guided molecular generation process as TAGMol.
  arXiv  Detail & Related papers  (2024-06-03T14:43:54Z)
• Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models [71.39421638547164]
  We propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs. Due to the generative bias towards reconstructing ID training samples, the similarity scores of OOD molecules will be much lower to facilitate detection. Our research pioneers an approach of Prototypical Graph Reconstruction for Molecular OOD Detection, dubbed as PGR-MOOD and hinges on three innovations.
  arXiv  Detail & Related papers  (2024-04-24T03:25:53Z)
• 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)
• 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)
• Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge [69.80471117520719]
  Re-Dock is a novel diffusion bridge generative model extended to geometric manifold. We propose energy-to-geometry mapping inspired by the Newton-Euler equation to co-model the binding energy and conformations. Experiments on designed benchmark datasets including apo-dock and cross-dock demonstrate our model's superior effectiveness and efficiency over current methods.
  arXiv  Detail & Related papers  (2024-02-18T05:04:50Z)
• Delta Score: Improving the Binding Assessment of Structure-Based Drug Design Methods [14.272327734087598]
  We introduce the delta score, a novel evaluation metric grounded in tangible pharmaceutical requisites. Our experiments reveal that molecules produced by current deep generative models significantly lag behind ground reference truth when assessed with the delta score.
  arXiv  Detail & Related papers  (2023-11-01T08:37:39Z)

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.