A Systematic Assessment of Deep Learning Models for Molecule Generation

• URL: http://arxiv.org/abs/2008.09168v1
• Date: Thu, 20 Aug 2020 19:13:31 GMT
• Title: A Systematic Assessment of Deep Learning Models for Molecule Generation
• Authors: Davide Rigoni, Nicol\`o Navarin and Alessandro Sperduti
• Abstract summary: We propose an extensive testbed for the evaluation of generative models for drug discovery. We present the results obtained by many of the models proposed in literature.
• Score: 70.59828655929194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drugs). This has produced many proposals in literature. However, a systematic comparison among the different VAE methods is still missing. For this reason, we propose an extensive testbed for the evaluation of generative models for drug discovery, and we present the results obtained by many of the models proposed in literature.

Related papers

• Less for More: Enhanced Feedback-aligned Mixed LLMs for Molecule Caption Generation and Fine-Grained NLI Evaluation [11.778576032848482]
  This work enhances such models by improving their inference and evaluation capabilities with minimal or no additional training. We reveal intriguing insights into the behaviour and suitability of such methods while significantly surpassing state-of-the-art models. We propose a novel atomic-level evaluation method leveraging off-the-shelf Natural Language Inference (NLI) models for use in the unseen chemical domain.
  arXiv  Detail & Related papers  (2024-05-22T20:40:53Z)
• 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)
• Diffusion Models in NLP: A Survey [1.5138755188783584]
  Diffusion models have become a powerful family of deep generative models, with record-breaking performance in many applications. This paper first gives an overview and derivation of the basic theory of diffusion models, then reviews the research results of diffusion models in the field of natural language processing.
  arXiv  Detail & Related papers  (2023-03-14T01:53:49Z)
• A Survey on Generative Diffusion Model [75.93774014861978]
  Diffusion models are an emerging class of deep generative models. They have certain limitations, including a time-consuming iterative generation process and confinement to high-dimensional Euclidean space. This survey presents a plethora of advanced techniques aimed at enhancing diffusion models.
  arXiv  Detail & Related papers  (2022-09-06T16:56:21Z)
• Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor [55.9023096444383]
  Current deep learning models are characterised by catastrophic forgetting of old knowledge when learning new classes. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning.
  arXiv  Detail & Related papers  (2022-04-28T08:41:51Z)
• De Novo Molecular Generation with Stacked Adversarial Model [24.83456726428956]
  Conditional generative adversarial models have recently been proposed as promising approaches for de novo drug design. We propose a new generative model which extends an existing adversarial autoencoder based model by stacking two models together. Our stacked approach generates more valid molecules, as well as molecules that are more similar to known drugs.
  arXiv  Detail & Related papers  (2021-10-24T14:23:16Z)
• Learning to Extend Molecular Scaffolds with Structural Motifs [15.78749196233448]
  MoLeR is a graph-based model that supports scaffolds as initial seed of the generative procedure. We show that MoLeR performs comparably to state-of-the-art methods on unconstrained molecular optimization tasks. We also show the influence of a number of seemingly minor design choices on the overall performance.
  arXiv  Detail & Related papers  (2021-03-05T18:28:49Z)
• 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)
• Conditional Constrained Graph Variational Autoencoders for Molecule Design [70.59828655929194]
  We present Conditional Constrained Graph Variational Autoencoder (CCGVAE), a model that implements this key-idea in a state-of-the-art model. We show improved results on several evaluation metrics on two commonly adopted datasets for molecule generation.
  arXiv  Detail & Related papers  (2020-09-01T21:58:07Z)
• Reverse Engineering Configurations of Neural Text Generation Models [86.9479386959155]
  The study of artifacts that emerge in machine generated text as a result of modeling choices is a nascent research area. We conduct an extensive suite of diagnostic tests to observe whether modeling choices leave detectable artifacts in the text they generate. Our key finding, which is backed by a rigorous set of experiments, is that such artifacts are present and that different modeling choices can be inferred by observing the generated text alone.
  arXiv  Detail & Related papers  (2020-04-13T21:02:44Z)

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.