Delta Score: Improving the Binding Assessment of Structure-Based Drug Design Methods

• URL: http://arxiv.org/abs/2311.12035v1
• Date: Wed, 1 Nov 2023 08:37:39 GMT
• Title: Delta Score: Improving the Binding Assessment of Structure-Based Drug Design Methods
• Authors: Minsi Ren, Bowen Gao, Bo Qiang, Yanyan Lan
• Abstract summary: 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.
• Score: 14.272327734087598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structure-based drug design (SBDD) stands at the forefront of drug discovery, emphasizing the creation of molecules that target specific binding pockets. Recent advances in this area have witnessed the adoption of deep generative models and geometric deep learning techniques, modeling SBDD as a conditional generation task where the target structure serves as context. Historically, evaluation of these models centered on docking scores, which quantitatively depict the predicted binding affinity between a molecule and its target pocket. Though state-of-the-art models purport that a majority of their generated ligands exceed the docking score of ground truth ligands in test sets, it begs the question: Do these scores align with real-world biological needs? In this paper, 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 truth reference ligands when assessed with the delta score. This novel metric not only complements existing benchmarks but also provides a pivotal direction for subsequent research in the domain.

Related papers

• 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)
• 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)
• Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion [18.269722389716165]
  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.
  arXiv  Detail & Related papers  (2024-03-04T07:40:25Z)
• 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)
• Open World Object Detection in the Era of Foundation Models [53.683963161370585]
  We introduce a new benchmark that includes five real-world application-driven datasets. We introduce a novel method, Foundation Object detection Model for the Open world, or FOMO, which identifies unknown objects based on their shared attributes with the base known objects.
  arXiv  Detail & Related papers  (2023-12-10T03:56:06Z)
• Leveraging Side Information for Ligand Conformation Generation using Diffusion-Based Approaches [12.71967232020327]
  Ligand molecule conformation generation is a critical challenge in drug discovery. Deep learning models have been developed to tackle this problem. These models often generate conformations that lack meaningful structure and randomness due to the absence of essential side information.
  arXiv  Detail & Related papers  (2023-08-02T09:56:47Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• 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)
• A biologically-inspired evaluation of molecular generative machine learning [17.623886600638716]
  A novel biologically-inspired benchmark for the evaluation of molecular generative models is proposed. We propose a recreation metric, apply drug-target affinity prediction and molecular docking as complementary techniques for the evaluation of generative outputs.
  arXiv  Detail & Related papers  (2022-08-20T11:01:10Z)

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.