Search research articles

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

Related Concept Videos

Targeted Cancer Therapies

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...

Targets for Drug Action: Overview

Targets for Drug Action: Overview

Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...

You might also read

Related Articles

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

Sort by

Same author

TikTok is a valuable data source for tracking the opioid crisis.

NPJ digital medicine·2026

Same author

Drug-Target Interaction Prediction with PIGLET.

bioRxiv : the preprint server for biology·2026

Same author

GATSBI: Improving context-aware protein embeddings through biologically motivated data splits.

bioRxiv : the preprint server for biology·2026

Same author

Biological data governance in an age of AI.

Science (New York, N.Y.)·2026

Same author

Publisher Correction: CRISPR-GPT for agentic automation of gene-editing experiments.

Nature biomedical engineering·2025

Same author

SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology.

bioRxiv : the preprint server for biology·2025

Same journal

Multimodal feature fusion for molecular property classification.

Journal of cheminformatics·2026

Same journal

P2MAT: A machine learning (ML) driven software for Property Prediction of MATerial.

Journal of cheminformatics·2026

Same journal

Computational design of low-volatility lubricants for space using interpretable machine learning.

Journal of cheminformatics·2026

Same journal

OpenStats: how to combine statistics and research data management (RDM) to leverage efficient scientific data analysis by guided statistics.

Journal of cheminformatics·2026

Same journal

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

Journal of cheminformatics·2026

Same journal

Count your bits: fingerprint benchmarking to assess broad chemical space representation.

Journal of cheminformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 7, 2026

Fixed Target Serial Data Collection at Diamond Light Source

Fixed Target Serial Data Collection at Diamond Light Source

Published on: February 26, 2021

The Human Omnibus of Targetable Pockets.

Kristy A Carpenter¹, Russ B Altman^2,3,4,5

¹Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.

Journal of Cheminformatics

|December 25, 2025

Summary

This summary is machine-generated.

We created the Human Omnibus of Targetable Pockets (HOTPocket) dataset, featuring over 2.4 million predicted ligand binding pockets across the human proteome. A new scoring method, hotpocketNN, identifies druggable pockets effectively.

Keywords:

Binding pocket Dataset Ligand binding Machine learning Neural network Protein language model

More Related Videos

Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System

Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System

Published on: March 6, 2019

Focused Ultrasound Induced Blood-Brain Barrier Opening for Targeting Brain Structures and Evaluating Chemogenetic Neuromodulation

Focused Ultrasound Induced Blood-Brain Barrier Opening for Targeting Brain Structures and Evaluating Chemogenetic Neuromodulation

Published on: December 22, 2020

Related Experiment Videos

Last Updated: Jan 7, 2026

Fixed Target Serial Data Collection at Diamond Light Source

Fixed Target Serial Data Collection at Diamond Light Source

Published on: February 26, 2021

Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System

Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System

Published on: March 6, 2019

Focused Ultrasound Induced Blood-Brain Barrier Opening for Targeting Brain Structures and Evaluating Chemogenetic Neuromodulation

Focused Ultrasound Induced Blood-Brain Barrier Opening for Targeting Brain Structures and Evaluating Chemogenetic Neuromodulation

Published on: December 22, 2020

Area of Science:

Computational biology
Structural bioinformatics
Drug discovery

Background:

Predicting ligand binding pockets is crucial for drug discovery, but a comprehensive human proteome-wide dataset is lacking.
Existing pocket-finding methods have limitations and excel in specific use cases.
Ensemble approaches combining multiple strategies offer potential for large-scale, diverse pocket prediction.

Purpose of the Study:

To create a comprehensive, human proteome-wide dataset of predicted ligand binding pockets.
To develop and validate a novel pocket scoring method for identifying druggable pockets.
To provide a freely available resource for the research community.

Main Methods:

Assembled the Human Omnibus of Targetable Pockets (HOTPocket) dataset using seven pocket-finding methods on PDB and AlphaFold2 structures.
Applied a novel scoring method, hotpocketNN, to filter and curate over 2.4 million predicted pockets.
Validated hotpocketNN against known pockets and benchmark datasets (Astex Diverse Set, PoseBusters).

Main Results:

The HOTPocket dataset contains over 2.4 million predicted pockets across the human proteome.
hotpocketNN successfully recovered known ligand binding pockets, including novel ones.
hotpocketNN outperformed constituent methods like P2Rank and Fpocket in precision assessments.
Identified druggable pockets in KRAS and the mu opioid receptor.

Conclusions:

The HOTPocket dataset and hotpocketNN method represent a significant advancement in proteome-wide druggable pocket identification.
hotpocketNN demonstrates superior performance and generalizability in predicting druggable pockets.
These freely available resources will accelerate drug discovery efforts by providing comprehensive pocket information.