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

Molecular Models02:00

Molecular Models

37.9K
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.
37.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

40
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...
40
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

10.3K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
10.3K
Molecular Orbital Theory I02:35

Molecular Orbital Theory I

31.7K
Overview of Molecular Orbital Theory
31.7K
Molecular Orbital Theory II03:51

Molecular Orbital Theory II

19.0K
Molecular Orbital Energy Diagrams
19.0K
Structure of Benzene: Molecular Orbital Model01:18

Structure of Benzene: Molecular Orbital Model

8.8K
According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
8.8K

You might also read

Related Articles

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

Sort by
Same author

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms.

Journal of chemical information and modeling·2026
Same author

AI decodes protein-ligand binding.

Nature chemical biology·2026
Same author

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

Nature protocols·2026
Same author

EpiMII: Structure-Aware Graph Neural Networks for MHC-II Epitope Generation.

Research (Washington, D.C.)·2026
Same author

Overcoming Resistance in the Androgen Receptor: Rational and Strategic Design of Advanced Antagonists.

Accounts of chemical research·2026
Same author

Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.

Nature communications·2026

Related Experiment Video

Updated: Jun 5, 2025

Interactive Molecular Model Assembly with 3D Printing
06:15

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

9.9K

3DSMILES-GPT: 3D molecular pocket-based generation with token-only large language model.

Jike Wang1, Hao Luo1, Rui Qin1

  • 1College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China yukang@zju.edu.cn tingjunhou@zju.edu.cn kimhsieh@zju.edu.cn.

Chemical Science
|December 12, 2024
PubMed
Summary

3DSMILES-GPT, a novel language model, generates 3D molecules for drug discovery, overcoming limitations of existing methods. It rapidly produces high-quality, valid, and synthesizable molecules with improved drug-likeness and binding affinity.

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

502
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K

Related Experiment Videos

Last Updated: Jun 5, 2025

Interactive Molecular Model Assembly with 3D Printing
06:15

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

9.9K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

502
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K

Area of Science:

  • Computational chemistry and cheminformatics
  • Artificial intelligence in drug discovery

Background:

  • Generating valid and high-quality 3D molecules is crucial for drug discovery but faces challenges with existing computational methods.
  • Current approaches often yield molecules with invalid configurations, poor drug-like properties, and slow generation times.

Purpose of the Study:

  • To introduce 3DSMILES-GPT, a language-model-driven framework for efficient and accurate 3D molecular generation.
  • To address limitations in molecular validity, conformation, drug-likeness, synthesizability, and generation speed.

Main Methods:

  • Utilized a token-only language model approach for both 2D and 3D molecular representations.
  • Pre-trained the model on millions of drug-like molecules, followed by fine-tuning with protein-pocket/molecule structural data.
  • Employed reinforcement learning to optimize biophysical and chemical properties.

Main Results:

  • 3DSMILES-GPT significantly outperforms existing methods in binding affinity, drug-likeness (QED), and synthetic accessibility score (SAS).
  • Achieved a 33% enhancement in quantitative estimation of drug-likeness (QED) while maintaining state-of-the-art binding affinity.
  • Demonstrated remarkable generation speed, averaging 0.45 seconds per molecule, a threefold increase over prior methods.

Conclusions:

  • 3DSMILES-GPT offers a powerful and efficient solution for 3D molecular generation in drug discovery.
  • The token-only, language-model-driven framework has the potential to accelerate the identification of novel drug candidates.