Search research articles

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

You might also read

Related Articles

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

Sort by

Same author

Beyond the Nucleus: Cytoplasmic Dominance in Follicular Thyroid Carcinoma Detection Using Single-Cell Raman Imaging across Multiple Devices.

Analytical chemistry·2025

Same author

Comparative Analysis of Reinforcement Learning Algorithms for Finding Reaction Pathways: Insights from a Large Benchmark Data Set.

Journal of chemical theory and computation·2025

Same author

Enumeration Approach to Atom-to-Atom Mapping Accelerated by Ising Computing.

Journal of chemical information and modeling·2025

Same author

Low-dimensional projection of reactivity classes in chemical reaction dynamics using supervised dimensionality reduction.

The Journal of chemical physics·2024

Same author

An encompassed representation of timescale hierarchies in first-order reaction network.

Proceedings of the National Academy of Sciences of the United States of America·2024

Same author

On-the-fly Raman microscopy guaranteeing the accuracy of discrimination.

Proceedings of the National Academy of Sciences of the United States of America·2024

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 22, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Multi-armed bandit algorithm for sequential experiments of molecular properties with dynamic feature selection.

Md Menhazul Abedin^1,2, Koji Tabata^3,4,5, Yoshihiro Matsumura⁵

¹Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo 060-8628, Japan.

The Journal of Chemical Physics

|July 3, 2024

Summary

This summary is machine-generated.

This study introduces a new sequential optimization algorithm using reinforcement learning and dynamic feature selection. It efficiently identifies optimal molecules by adapting to experimental data, outperforming standard methods.

More Related Videos

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

Related Experiment Videos

Last Updated: Jun 22, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

Area of Science:

Computational Chemistry
Machine Learning
Drug Discovery

Background:

Sequential optimization aims to find optimal candidates with desired properties while minimizing experiments.
High dimensionality in feature spaces (e.g., molecular descriptors) complicates feature utilization in candidate selection.
Existing methods like Bayesian Optimization (BO) often use fixed feature sets, limiting adaptability.

Purpose of the Study:

To develop a novel sequential optimization algorithm for molecular problems.
To address the challenge of high dimensionality and dynamic feature relevance in optimization.
To improve the efficiency and reliability of identifying optimal molecular candidates.

Main Methods:

Developed a new algorithm integrating reinforcement learning, multi-armed linear bandits, and online dynamic feature selection.
Implemented a stopping condition to ensure the reliability of the selected candidate.
Compared the algorithm against Bayesian Optimization (BO) using synthetic and real-world molecular datasets (hydration free energy, enantiomer product free energy difference).

Main Results:

The developed algorithm demonstrated superior performance, particularly in reducing the time to find the best candidate and stop experiments.
Dynamic feature selection, adapting molecular descriptors with experiments, significantly improved optimization efficiency.
The multi-armed linear bandit approach with dynamic feature selection outperformed standard BO with fixed features.

Conclusions:

Dynamic feature selection is crucial for efficient sequential optimization in high-dimensional molecular spaces.
The proposed algorithm offers a more effective and reliable approach for identifying optimal molecular candidates compared to traditional methods.
This work advances the application of machine learning in computational chemistry and drug discovery.