A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design

• URL: http://arxiv.org/abs/2505.12848v1
• Date: Mon, 19 May 2025 08:34:38 GMT
• Title: A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design
• Authors: Adarsh Singh,
• Abstract summary: Deep generative models have emerged as promising tools for accelerating drug discovery by efficiently exploring the vast chemical space.<n>This research presents an analysis of the Molecular Sets (MOSES) platform, a comprehensive benchmarking framework designed to standardize evaluation of deep generative models in molecular design.<n>Our findings reveal that different architectures exhibit complementary strengths across various metrics, highlighting the complex trade-offs between exploration and exploitation in chemical space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of novel pharmaceuticals represents a significant challenge in modern science, with substantial costs and time investments. Deep generative models have emerged as promising tools for accelerating drug discovery by efficiently exploring the vast chemical space. However, this rapidly evolving field lacks standardized evaluation protocols, impeding fair comparison between approaches. This research presents an extensive analysis of the Molecular Sets (MOSES) platform, a comprehensive benchmarking framework designed to standardize evaluation of deep generative models in molecular design. Through rigorous assessment of multiple generative architectures, including recurrent neural networks, variational autoencoders, and generative adversarial networks, we examine their capabilities in generating valid, unique, and novel molecular structures while maintaining specific chemical properties. Our findings reveal that different architectures exhibit complementary strengths across various metrics, highlighting the complex trade-offs between exploration and exploitation in chemical space. This study provides detailed insights into the current state of the art in molecular generation and establishes a foundation for future advancements in AI-driven drug discovery.

Related papers

• DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra [60.39311767532607]
  DiffMS is a formula-restricted encoder-decoder generative network.<n>We develop a robust decoder that bridges latent embeddings and molecular structures.<n>Experiments show DiffMS outperforms existing models on $textitde novo$ molecule generation.
  arXiv  Detail & Related papers  (2025-02-13T18:29:48Z)
• Diffusion Models for Molecules: A Survey of Methods and Tasks [56.44565051667812]
  Generative tasks about molecules are crucial for drug discovery and material design.<n>Diffusion models have emerged as an impressive class of deep generative models.<n>This paper conducts a comprehensive survey of diffusion model-based molecular generative methods.
  arXiv  Detail & Related papers  (2025-02-13T17:22:50Z)
• GraphXForm: Graph transformer for computer-aided molecular design [73.1842164721868]
  We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds.<n>We evaluate it on various drug design tasks, demonstrating superior objective scores compared to state-of-the-art molecular design approaches.
  arXiv  Detail & Related papers  (2024-11-03T19:45:15Z)
• Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective [16.91569591356659]
  Computational molecular design is the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches. We argue that these techniques are mature enough to be applied to not just extend the longevity of life, but the beauty it manifests. In this perspective, we review the current frontiers in the research & development of skin care products, as well as the statistical and physical toolbox applicable to addressing the challenges in this industry.
  arXiv  Detail & Related papers  (2024-10-08T13:30:27Z)
• Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design [0.0]
  We show that chemistry foundation models can serve as a basis for enabling structure-focused, semantic chemistry information retrieval.<n>We also show the use of chemistry foundation models in conjunction with multi-modal models such as OpenCLIP.
  arXiv  Detail & Related papers  (2024-08-21T17:25:45Z)
• A Review on Fragment-based De Novo 2D Molecule Generation [0.0]
  fragment-based deep generative models consistently achieve state-of-the-art results in molecular design benchmarks as of 2023. This review includes comparisons of output quality, generation speed, and the current limitations of specific models. We highlight promising avenues for future research that could bridge fragment-based models to real-world applications.
  arXiv  Detail & Related papers  (2024-05-08T09:38:38Z)
• Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization [10.868749018881115]
  We present an innovative approach to tackle the inverse design problem by formulating it as a multi-modality guidance optimization task.<n>Our proposed solution involves a textural-structure alignment symmetric diffusion framework for the implementation of molecular optimization tasks, namely 3DToMolo.<n>3DToMolo aims to harmonize diverse modalities including textual description features and graph structural features, aligning them seamlessly to produce molecular structures adhere to specified symmetric structural and textural constraints by experts in the field.
  arXiv  Detail & Related papers  (2024-03-06T03:15:25Z)
• An Equivariant Generative Framework for Molecular Graph-Structure Co-Design [54.92529253182004]
  We present MolCode, a machine learning-based generative framework for underlineMolecular graph-structure underlineCo-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Our investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design.
  arXiv  Detail & Related papers  (2023-04-12T13:34:22Z)
• 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 Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)
• Generative chemistry: drug discovery with deep learning generative models [0.0]
  This paper reviews the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused.
  arXiv  Detail & Related papers  (2020-08-20T14:38:21Z)
• Visualizing Deep Graph Generative Models for Drug Discovery [16.78530326723672]
  We propose a visualization framework to visualize molecules generated during the encoding and decoding process of deep graph generative models. Our work tries to empower black box AI driven drug discovery models with some visual interpretabilities.
  arXiv  Detail & Related papers  (2020-07-20T18:49:10Z)

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.