3D pride without 2D prejudice: Bias-controlled multi-level generative
models for structure-based ligand design
- URL: http://arxiv.org/abs/2204.10663v1
- Date: Fri, 22 Apr 2022 12:23:59 GMT
- Title: 3D pride without 2D prejudice: Bias-controlled multi-level generative
models for structure-based ligand design
- Authors: Lucian Chan, Rajendra Kumar, Marcel Verdonk and Carl Poelking
- Abstract summary: Data sparsity and bias are two main roadblocks to the development of 3D-aware models.
We propose a first-in-kind training protocol based on multi-level contrastive learning for improved bias control and data efficiency.
- Score: 1.978587235008588
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative models for structure-based molecular design hold significant
promise for drug discovery, with the potential to speed up the hit-to-lead
development cycle, while improving the quality of drug candidates and reducing
costs. Data sparsity and bias are, however, two main roadblocks to the
development of 3D-aware models. Here we propose a first-in-kind training
protocol based on multi-level contrastive learning for improved bias control
and data efficiency. The framework leverages the large data resources available
for 2D generative modelling with datasets of ligand-protein complexes. The
result are hierarchical generative models that are topologically unbiased,
explainable and customizable. We show how, by deconvolving the generative
posterior into chemical, topological and structural context factors, we not
only avoid common pitfalls in the design and evaluation of generative models,
but furthermore gain detailed insight into the generative process itself. This
improved transparency significantly aids method development, besides allowing
fine-grained control over novelty vs familiarity.
Related papers
- Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
We propose a novel EHR data generation model called EHRPD.
It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation.
We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
arXiv Detail & Related papers (2024-06-20T02:20:23Z) - DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
Current perceptive models heavily depend on resource-intensive datasets.
We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability.
Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
arXiv Detail & Related papers (2024-03-20T04:58:03Z) - GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image [94.56927147492738]
We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes from single images.
We show that leveraging diffusion priors can markedly improve generalization, detail preservation, and efficiency in resource usage.
We propose a simple yet effective strategy to segregate the complex data distribution of various scenes into distinct sub-distributions.
arXiv Detail & Related papers (2024-03-18T17:50:41Z) - A Generative Machine Learning Model for Material Microstructure 3D
Reconstruction and Performance Evaluation [4.169915659794567]
The dimensional extension from 2D to 3D is viewed as a highly challenging inverse problem from the current technological perspective.
A novel generative model that integrates the multiscale properties of U-net with and the generative capabilities of GAN has been proposed.
The model's accuracy is further improved by combining the image regularization loss with the Wasserstein distance loss.
arXiv Detail & Related papers (2024-02-24T13:42:34Z) - Generative Structural Design Integrating BIM and Diffusion Model [4.619347136761891]
This study introduces building information modeling ( BIM) into intelligent structural design and establishes a structural design pipeline integrating BIM and generative AI.
In terms of generation framework, inspired by the process of human drawing, a novel 2-stage generation framework is proposed to reduce the generation difficulty for AI models.
In terms of generative AI tools adopted, diffusion models (DMs) are introduced to replace widely used generative adversarial network (GAN)-based models, and a novel physics-based conditional diffusion model (PCDM) is proposed to consider different design prerequisites.
arXiv Detail & Related papers (2023-11-07T15:05:19Z) - Generative Forests [26.09279398946235]
We introduce new tree-based generative models convenient for density modeling and data generation.
We also introduce a training algorithm which simplifies the training setting of previous approaches.
Experiments are provided on missing data imputation and comparing generated data to real data, displaying the quality of the results.
arXiv Detail & Related papers (2023-08-07T14:58:53Z) - A Systematic Survey in Geometric Deep Learning for Structure-based Drug
Design [63.30166298698985]
Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to identify potential drug candidates.
Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, have greatly advanced the field of structure-based drug design.
arXiv Detail & Related papers (2023-06-20T14:21:58Z) - 3D Equivariant Diffusion for Target-Aware Molecule Generation and
Affinity Prediction [9.67574543046801]
The inclusion of 3D structures during targeted drug design shows superior performance to other target-free models.
We develop a 3D equivariant diffusion model to solve the above challenges.
Our model could generate molecules with more realistic 3D structures and better affinities towards the protein targets, and improve binding affinity ranking and prediction without retraining.
arXiv Detail & Related papers (2023-03-06T23:01:43Z) - DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure
reconstruction from extremely small data sets [110.60233593474796]
DA-VEGAN is a model with two central innovations.
A $beta$-variational autoencoder is incorporated into a hybrid GAN architecture.
A custom differentiable data augmentation scheme is developed specifically for this architecture.
arXiv Detail & Related papers (2023-02-17T08:49:09Z) - Secrets of 3D Implicit Object Shape Reconstruction in the Wild [92.5554695397653]
Reconstructing high-fidelity 3D objects from sparse, partial observation is crucial for various applications in computer vision, robotics, and graphics.
Recent neural implicit modeling methods show promising results on synthetic or dense datasets.
But, they perform poorly on real-world data that is sparse and noisy.
This paper analyzes the root cause of such deficient performance of a popular neural implicit model.
arXiv Detail & Related papers (2021-01-18T03:24:48Z)
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.