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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
Measurement of Bioavailability: Pharmacodynamic Methods01:20

Measurement of Bioavailability: Pharmacodynamic Methods

Pharmacodynamic methods provide insights into a drug's effects on physiological processes over time and play a crucial role in understanding bioavailability and therapeutic efficacy. These methods can be broadly classified into acute pharmacological and therapeutic response approaches, each with distinct mechanisms and applications.The acute pharmacological response method directly correlates a drug's physiological effects, such as ECG or pupil diameter changes, to its time course in the body.
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
Bioavailability Enhancement: Determination and Conceptual Approaches in Overcoming Bioavailability Problems01:22

Bioavailability Enhancement: Determination and Conceptual Approaches in Overcoming Bioavailability Problems

Bioavailability is a critical pharmacological concept that measures the extent and rate at which an active drug ingredient or therapeutic moiety enters the systemic circulation, remaining unchanged. It's a pivotal factor in determining a drug's efficacy and safety.The Biopharmaceutics Classification System (BCS) plays an essential role in drug development by categorizing drugs into four classes based on their solubility and permeability. This classification aids in understanding drug absorption...

You might also read

Related Articles

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

Sort by
Same author

Atomic Uncertainty Pinpoints Critical Failure Structures for Trustworthy Molecular Property Prediction.

Journal of chemical information and modeling·2026
Same author

Cardiac unloading models for myocardial reverse remodeling.

Basic research in cardiology·2026
Same author

HP-MoleQ: An Effective Predictive Model for High-Throughput Screening of Food-Derived Hepatoprotective Compounds.

Interdisciplinary sciences, computational life sciences·2026
Same author

Long-term outcomes of radiofrequency ablation versus surgery for bilateral multifocal (≤3) T1N0M0 papillary thyroid carcinoma: A retrospective cohort study.

Surgery·2026
Same author

Differentiation of Fat-Poor Renal Angiomyolipoma From Clear Cell Renal Cell Carcinoma: Diagnostic Performance of a Novel Type of Color Contrast Enhanced Ultrasound.

Cancer medicine·2026
Same author

Angio planewave ultrasensitive imaging (Angio PLUS) as an innovative technique in depicting vascularity of median nerve: a prospective observational study.

BMC medical imaging·2026

Related Experiment Video

Updated: Jul 16, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Bayesian Uncertainty-Guided Fidelity Fusion for Bioactivity Prediction.

Shiyang Bian1, Yukun Luo2, Hongqiao Wang1

  • 1School of Mathematics and Statistics, Central South University, Changsha 410083, People's Republic of China.

Journal of Chemical Information and Modeling
|July 14, 2026
PubMed
Summary

We introduce a Bayesian framework for molecular property prediction, enhancing drug design. This approach efficiently fuses classification and regression data, providing reliable uncertainty estimates for data-scarce scenarios.

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: Jul 16, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Machine learning

Background:

  • Accurate molecular bioactivity prediction is crucial for rational drug design.
  • Challenges include data scarcity and imbalanced labels in molecular property prediction tasks.

Purpose of the Study:

  • To develop a data-efficient Bayesian framework for molecular property prediction.
  • To integrate classification-to-regression knowledge fusion and uncertainty quantification.
  • To improve molecular modeling for drug discovery in resource-limited settings.

Main Methods:

  • Proposed the Bayesian Class-Attentive Transformer Network (BCATNet).
  • BCATNet fuses classification data priors into a Bayesian regression task using cross-token attention.
  • Employed uncertainty quantification and active learning for data efficiency.

Main Results:

  • BCATNet demonstrated superior performance and robustness under reduced regression supervision compared to baseline models.
  • Bayesian uncertainty estimates correlated with prediction errors and enabled risk stratification.
  • Uncertainty-driven active learning strategies achieved optimal performance on benchmark tasks.

Conclusions:

  • BCATNet offers a generalizable paradigm for uncertainty-aware molecular modeling by bridging classification and regression.
  • The framework provides a principled approach for reliable, interpretable, and resource-efficient drug discovery.
  • Explicit classification-to-regression knowledge fusion is a competitive alternative to generic molecular pretraining.