Loading BioAtlas...

Uni-Mol - AI Drug Discovery | aibioatlas | aibioatlas

首页›AI Drug Discovery›Uni-Mol

Uni-Mol Scientific Architecture

Uni-Mol Scientific Architecture · DeepModeling / Uni-Mol contributors

AI Drug Discovery · Molecular Property Prediction

Uni-Mol

开发机构 DeepModeling / Uni-Mol contributors →

Learning molecular representations from three-dimensional conformations

Learning molecular representations from three-dimensional conformations. The repository contains several related models; select a documented release and evaluate domain shift.

Learning molecular representations from three-dimensional conformationsInput: Molecular structures, conformers and task labelsOutput: Molecular embeddings and task-specific predictions

⚖️ 竞品横向对比 Compare

报告错误数据或申请机构认证

概览介绍

Learning molecular representations from three-dimensional conformations. The repository contains several related models; select a documented release and evaluate domain shift.

信息已比对权威文献或官方白皮书；非实验性跑分。权威出处 · 最后核对: 16/09/2026, 17:33:40 UTC

核心功能特性

Learning molecular representations from three-dimensional conformations
Input: Molecular structures, conformers and task labels
Output: Molecular embeddings and task-specific predictions

Interactive 3D Illustration

Biological Architecture (Stylized Ribbon)

Default view is a stylized demonstration ribbon. Enter any 4-letter RCSB PDB accession code above to stream real atomic coordinates.

⇄ Drag to rotate · Scroll to zoom

Illustrative Confidence Gradient:

>90 High / Low B 70-90 Confident 50-70 Low <50 Very low / High B

Academic Context & Research Evidence

Biological & Workflow Fit

Compute & Hardware: Consult linked installation guidance; workload dependent
Licensing & Academic Use: Review the linked release, model weights, datasets and service terms separately
Documented Evidence: Project or provider documentation; no local performance benchmark

Cite this Tool

Use this citation format when referencing Uni-Mol in scientific publications and benchmark papers.

@software{uni_mol_2026,
  title = {{Uni-Mol}},
  author = {{DeepModeling / Uni-Mol contributors}},
  year = {2026},
  url = {https://github.com/deepmodeling/Uni-Mol},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations queried from Europe PMC and PubMed for Uni-Mol.

⏳ Querying Europe PMC & PubMed for indexed publications...

技术参数与产品规格

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeResearch software / model

Access modePublic code / project terms

AI roleScientific machine learning

Input dataMolecular structures, conformers and task labels

Output dataMolecular embeddings and task-specific predictions

Licence conditionsReview the linked release, model weights, datasets and service terms separately

Commercial eligibilityNot independently confirmed

Compute requirementsConsult linked installation guidance; workload dependent

ValidationEvaluate on representative data; no clinical suitability established

TypeNot recorded

Intended useBiological and pharmaceutical research

CompatibilityNot recorded

LimitationsThe repository contains several related models; select a documented release and evaluate domain shift.

Evidence levelProject or provider documentation; no local performance benchmark

AvailabilitySee project access and maintenance status

Price / accessSee project terms; compute costs may apply

Research fit & compatibility

No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

Gallery & resources

Uni-Mol Scientific Architecture

Uni-Mol Scientific Architecture · Credit: DeepModeling / Uni-Mol contributors · Open original ↗

Research and editorial use Source & attribution ↗ License ↗

Resource: Project documentation and evidence ↗ · github.com

BEFORE YOU CHOOSE

Evaluate AI Drug Discovery

Which input, output and scientific task does the selected version support?
Is this a model, supporting tool, commercial platform or research prototype?
Verify the code, weights, data licence and independent validation before selecting a workflow.

Selection guide →Find alternatives →

竞品替代

同一科学领域的同类算法与工具。

MOLECULAR DOCKING • SMILES GRAPH

ADMET-AI

AI Drug Discovery

Research AI Methods

ADMET-AI

Source-scoped research method · Confirm distribution and exact release

ADMET-AI — admet-property-prediction

Research / method authors

学术论文 ↗

⚖️ 与同类替代工具对比

深入探索 →＋加入对比

MOLECULAR DOCKING • SMILES GRAPH

ChemFM

AI Drug Discovery

Molecular Property Prediction

ChemFM

Research AI model / method · Research code or publication

Chemical foundation model for learned molecular representations, property prediction and conditional mole

Luo Lab

GitHub ↗学术论文 ↗

⚖️ 与同类替代工具对比

深入探索 →＋加入对比

MOLECULAR DOCKING • SMILES GRAPH

DiffPharma

AI Drug Discovery

Molecular Design

DiffPharma

Research AI model / method · Research code or publication

Diffusion-based ligand molecule generation constrained by specified pocket and pharmacophore interactions

Sekijima Lab

GitHub ↗学术论文 ↗

⚖️ 与同类替代工具对比

深入探索 →＋加入对比

MOLECULAR DOCKING • SMILES GRAPH

DiffSBDD

AI Drug Discovery

Research AI Methods

DiffSBDD

Source-scoped research method · Confirm distribution and exact release

DiffSBDD — structure-conditioned-molecular-generation

Research / method authors

⚖️ 与同类替代工具对比

深入探索 →＋加入对比

⚖️ 将 Uni-Mol 与全部 4 个同类工具横向对比 →

最新研究动态

暂无与此工具关联的已验证学术动态。

浏览全部生物AI动态 →

常见问题解答

该工具在哪些平台或地区可用？

See project access and maintenance status

商业授权与费用如何计算？

费用根据官方最近一次公布的说明整理。请在官方网站确认最新报价与学术折扣。

Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating Uni-Mol.

No peer reviews submitted yet for Uni-Mol.

Be the first verified researcher or computational biologist to submit an evaluation of biological accuracy and workflow performance.