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

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Associative Learning01:27

Associative Learning

1.3K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.3K
Purposive Learning01:22

Purposive Learning

512
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
512
Observational Learning01:12

Observational Learning

992
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
992
Learning Disabilities01:25

Learning Disabilities

618
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...
618
Introduction to Learning01:18

Introduction to Learning

1.2K
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Accurate prediction of anticancer peptides using a stacking ensemble of convolutional and transformer models with conjoint sequence representations.

Computers in biology and medicine·2026
Same author

Active Stacking-Deep Learning with Strategic Sampling for Small and Imbalanced Chemical Toxicity Prediction.

ACS omega·2025
Same author

Accurate structure-activity relationship prediction of antioxidant peptides using a multimodal deep learning framework.

Journal of cheminformatics·2025
Same author

MetaAMPK: Accurate Prediction of Adenosine Monophosphate-Activated Protein Kinase Activators Using a Meta-Learner Neural Network.

ACS omega·2025
Same author

Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for Possibly Carcinogenic Chemicals.

Journal of chemical information and modeling·2025
Same author

Multimodal Deep Learning for Generating Potential Anti-Dengue Peptides.

ACS omega·2025

Related Experiment Video

Updated: Feb 5, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Data-efficient learning for accurate identification of MAPK1 inhibitors using an active meta-deep learning framework.

Darlene Nabila Zetta1, Tarapong Srisongkram2

  • 1Graduate School in the Program of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen, 40002, Thailand.

Journal of Cheminformatics
|February 3, 2026
PubMed
Summary

This study introduces a novel active meta-deep learning framework to efficiently predict cancer-fighting MAPK1 inhibitors, even with limited data. The approach accelerates drug discovery by intelligently selecting informative molecules for model training.

Keywords:
Active learningDeep learningDrug discoveryMAPK1

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K

Related Experiment Videos

Last Updated: Feb 5, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K

Area of Science:

  • Computational chemistry
  • Machine learning in drug discovery
  • Bioinformatics

Background:

  • Limited experimental data hinders machine learning applications in drug discovery, especially for cancer targets.
  • Mitogen-activated protein kinase 1 (MAPK1) inhibitors are promising for cancer therapies but require efficient predictive models.

Purpose of the Study:

  • To develop a data-efficient active meta-deep learning framework for predicting MAPK1 inhibitors.
  • To address the challenge of limited experimental data in cancer drug discovery.

Main Methods:

  • Integrated active learning (AL) with a meta-model combining four deep architectures (CNN, attention, GCN, GNN-attention).
  • Trained models on molecular descriptors and graph representations, generating probability-based features for a meta-learner.
  • Evaluated AL sampling strategies, with entropy sampling showing competitive performance.

Main Results:

  • The framework achieved significant improvements in AUPRC (5.12%) and MCC (5.48%) using only 10% of training data.
  • Achieved competitive performance (AUPRC: 0.835, MCC: 0.817) with less data compared to traditional methods.
  • Demonstrated generalizability on an external MAPK1 dataset, with AUPRC of 0.818 and MCC of 0.403.

Conclusions:

  • The proposed framework effectively accelerates predictive performance in data-scarce drug discovery by combining intelligent data selection with deep learning.
  • This approach holds significant potential for advancing cancer-related therapies by enabling efficient identification of novel drug candidates.