Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Assessment of Airway, Skin Color, and Use of Accessory Muscles01:30

Assessment of Airway, Skin Color, and Use of Accessory Muscles

1.8K
A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
Introduction
The initial evaluation of a patient's respiratory system...
1.8K
Learning Disabilities01:25

Learning Disabilities

660
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
660
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

227
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
227

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

SynCraft: an integrated web server for ADMET-aware retrosynthesis and molecular design.

Nucleic acids research·2026
Same author

DeepExpDR: Drug Response Prediction through Molecular Topological Grouping and Substructure-Aware Expert.

Journal of chemical information and modeling·2025
Same author

Partner-RBR: Predicting Multitype RNA-Binding Residues Based on Mutual Learning.

Journal of chemical information and modeling·2025
Same author

Precise prediction of hotspot residues in protein-RNA complexes using graph attention networks and pretrained protein language models.

Bioinformatics (Oxford, England)·2025
Same author

DeepHeteroCDA: circRNA-drug sensitivity associations prediction via multi-scale heterogeneous network and graph attention mechanism.

Briefings in bioinformatics·2025
Same author

DeepRSMA: a cross-fusion-based deep learning method for RNA-small molecule binding affinity prediction.

Bioinformatics (Oxford, England)·2024

Related Experiment Video

Updated: Apr 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

TwistDAN: Twisted Domain Adversarial Network for Synthetic Accessibility Assessment.

Qahtan Adnan Aljanabi1, Zhijian Huang1, Ziyu Fan1

  • 1School of Computer Science and Engineering, Central South University, Changsha 410083, Hunan, China.

Journal of Chemical Information and Modeling
|February 26, 2026
PubMed
Summary

TwistDAN improves synthetic accessibility prediction by using domain adversarial neural networks for better generalization across diverse chemical libraries. This reduces unnecessary synthesis attempts in drug discovery.

Related Experiment Videos

Last Updated: Apr 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

Area of Science:

  • Computational Chemistry
  • Medicinal Chemistry
  • Machine Learning

Background:

  • Synthetic accessibility (SA) prediction is crucial for guiding molecule synthesis in drug discovery.
  • Existing SA predictors lack generalization across diverse chemical domains, limiting their use in virtual screening.
  • Accurate SA prediction is essential for efficient hit identification and lead optimization.

Purpose of the Study:

  • To develop a novel SA prediction model, TwistDAN (Twisted Domain Adversarial Network), with improved cross-domain generalization.
  • To leverage semisupervised learning by combining supervised and adversarial training on labeled and unlabeled molecular data.
  • To enhance the utility of SA prediction for virtual screening of large and diverse molecular libraries.

Main Methods:

  • Adapted domain adversarial neural networks (DANN) for SA prediction using semisupervised learning.
  • Employed supervised learning on 640,000 labeled molecules (Easy-to-Synthesize and Hard-to-Synthesize) and adversarial learning on 2.1 million unlabeled SELFIES variants.
  • Utilized identical 2D molecular graph representations, graph attention networks, and gradient reversal layers for domain-invariant learning.

Main Results:

  • TwistDAN demonstrated strong cross-domain generalization with an AUROC of 0.951 under severe domain shift.
  • Achieved high performance on challenging discrimination tasks (AUROC = 0.938) with structurally similar molecules.
  • Exhibited high precision (0.980), reducing false-positive predictions by 12 percentage points compared to leading methods.

Conclusions:

  • TwistDAN significantly enhances the generalization capability of synthetic accessibility prediction models.
  • The model's high precision minimizes wasted synthesis efforts, accelerating drug discovery pipelines.
  • TwistDAN offers interpretable attention-based visualizations for medicinal chemists, aiding in rational drug design.