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DeeplyTough - AI Drug Discovery | aibioatlas | aibioatlas

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DeeplyTough Scientific Architecture

DeeplyTough Scientific Architecture · BenevolentAI / DeeplyTough authors

AI Drug Discovery · Molecular Docking

DeeplyTough

开发机构 BenevolentAI / DeeplyTough authors →

Learned comparison of protein binding-site structures

Learned comparison of protein binding-site structures. A binding-site comparison tool, not a ligand pose generator; legacy dependencies may need maintenance.

Learned comparison of protein binding-site structuresInput: Prepared protein binding-site structuresOutput: Pocket representations and similarity comparisons

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概览介绍

Learned comparison of protein binding-site structures. A binding-site comparison tool, not a ligand pose generator; legacy dependencies may need maintenance.

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: 17/09/2026, 16:20:54 UTC

核心功能特性

Learned comparison of protein binding-site structures
Input: Prepared protein binding-site structures
Output: Pocket representations and similarity comparisons

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.

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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: See source requirements for selected configuration
Licensing & Academic Use: Review code, weights, data and service terms separately
Documented Evidence: Project/provider documentation; no local benchmark

Cite this Tool

Use this citation format when referencing DeeplyTough in scientific publications and benchmark papers.

@software{deeplytough_2026,
  title = {{DeeplyTough}},
  author = {{BenevolentAI / DeeplyTough authors}},
  year = {2026},
  url = {https://github.com/BenevolentAI/DeeplyTough},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations queried from Europe PMC and PubMed for DeeplyTough.

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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 / framework

Access modePublic code / project terms

AI roleScientific machine learning

Input dataPrepared protein binding-site structures

Output dataPocket representations and similarity comparisons

Licence conditionsReview code, weights, data and service terms separately

Commercial eligibilityNot independently confirmed

Compute requirementsSee source requirements for selected configuration

ValidationNot recorded

TypeNot recorded

Intended useBiological and pharmaceutical research

CompatibilityNot recorded

LimitationsA binding-site comparison tool, not a ligand pose generator; legacy dependencies may need maintenance.

Evidence levelProject/provider documentation; no local benchmark

AvailabilitySee project release 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

DeeplyTough Scientific Architecture

DeeplyTough Scientific Architecture · Credit: BenevolentAI / DeeplyTough authors · Open original ↗

Research and editorial use Source & attribution ↗ License ↗

Resource: Project documentation and evidence ↗ · github.com

BEFORE YOU CHOOSE

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竞品替代

同一科学领域的同类算法与工具。

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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 ↗

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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 ↗

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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 ↗

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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

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最新研究动态

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常见问题解答

该工具在哪些平台或地区可用？

See project release 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。

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