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

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

Inductive Effects on Chemical Shift: Overview

Inductive Effects on Chemical Shift: Overview

The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...

Molecules with Multiple Chiral Centers

Molecules with Multiple Chiral Centers

Molecules that possess multiple chiral centers can afford a large number of stereoisomers. For instance, while some molecules like 2-butanol have one chiral center, defined as a tetrahedral carbon atom with four different substituents attached, several molecules like butane-2,3-diol have multiple chiral centers. A simple formula to predict the number of stereoisomers possible for a molecule with n chiral centers is 2n. However, there can be a lower number where some of the stereoisomers are...

Molecular Orbital Theory II

Molecular Orbital Theory II

Molecular Orbital Energy Diagrams

Chemical Synapses

Chemical Synapses

Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...

You might also read

Related Articles

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

Sort by

Same author

Prediction of antibody non-specificity using protein language models and biophysical parameters.

mAbs·2026

Same author

A Once-Weekly C-Type Natriuretic Peptide for Treatment of Heart Failure with Preserved Ejection Fraction.

Journal of medicinal chemistry·2025

Same author

Molecular Representations in Machine-Learning-Based Prediction of PK Parameters for Insulin Analogs.

ACS omega·2023

Same author

Development of Cagrilintide, a Long-Acting Amylin Analogue.

Journal of medicinal chemistry·2021

Same author

Modeling conformational flexibility of kinases in inactive states.

Proteins·2019

Same author

From Cancer to Pain Target by Automated Selectivity Inversion of a Clinical Candidate.

Journal of medicinal chemistry·2018

Search research articles

Related Experiment Video

Updated: Jan 6, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition.

Sabrina Jaeger¹, Simone Fulle¹, Samo Turk¹

¹BioMed X Innovation Center , Im Neuenheimer Feld 515, 69120 Heidelberg, Germany.

Journal of Chemical Information and Modeling

|December 23, 2017

Summary

This summary is machine-generated.

Mol2vec is a new unsupervised machine learning method that creates vector representations for molecular substructures. This approach enhances the prediction of compound properties and bioactivity by overcoming limitations of traditional feature representations.

More Related Videos

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Published on: December 1, 2023

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Related Experiment Videos

Last Updated: Jan 6, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Published on: December 1, 2023

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Area of Science:

Computational chemistry
Machine learning
Bioinformatics

Background:

Traditional molecular representations like Morgan fingerprints can suffer from sparseness and bit collisions.
Developing effective feature representations is crucial for accurate prediction of compound properties and bioactivity.

Purpose of the Study:

Introduce Mol2vec, an unsupervised machine learning approach for learning vector representations of molecular substructures.
Enable encoding of compounds into vectors for use in supervised machine learning models.
Demonstrate the utility of Mol2vec for predicting compound properties and bioactivity.

Main Methods:

Utilize natural language processing-inspired techniques, specifically Word2vec, for molecular substructures.
Train an unsupervised model on a large corpus of chemical compounds to obtain substructure vector embeddings.
Encode compounds by summing the vectors of their constituent substructures.

Main Results:

Mol2vec generates dense vector representations, overcoming sparseness and bit collisions.
The method demonstrates strong prediction capabilities on various compound property and bioactivity datasets.
Mol2vec performance is comparable to or better than Morgan fingerprints.

Conclusions:

Mol2vec provides a powerful and efficient method for molecular representation learning.
The approach can be readily combined with protein sequence representations (ProtVec) for proteochemometric modeling.
Mol2vec offers an alignment-independent solution for analyzing protein sequence similarities.