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Compare biological models, docking engines, and computational workflows side-by-side. Filter by access model, compute footprint, and category to reach an actionable decision table in under a minute.
| Attribute | ＋ Add tool | |||
|---|---|---|---|---|
| Category | Drug Discovery & Chemistry | AI Drug Discovery | AI Drug Discovery | — |
| Subcategory | End-to-End AI Drug Discovery Platform | Research AI Methods | Molecular Property Prediction | — |
| Company | Insilico Medicine | Research / method authors | Luo Lab | — |
| Access mode | Commercial Subscription & Co-Development | Confirm distribution and exact release | Research code or publication | — |
| Licence conditions | Proprietary commercial software | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | — |
| Compute requirements | Cloud SaaS with enterprise security compliance | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Fibrosis, oncology, immunology, and age-related disease drug discovery | Not recorded | Not recorded | — |
| Research workflow | Disease multi-omics data -> novel target selection -> generative chemistry -> candidate synthesis | Not recorded | Not recorded | — |
| Evidence level | Clinical-stage pipeline with published experimental and clinical validation (Nature Biotech 2024) | PEER_REVIEWED_METHOD_OR_CONFERENCE | Peer-reviewed primary method | — |
| Commercial eligibility | Commercial enterprise licensing and drug discovery partnerships | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | — |
| Price | Enterprise partnership — contact vendor | Verify access and licence | Check selected model licence | — |
| Availability ⓘ | Globally available for biopharma partners | Method paper or official documentation | Public research method/code | — |
| Features | PandaOmics for multi-omics target identification and biological aging biomarker analysis, Chemistry42 generative chemistry platform with 42 distinct generative algorithms, Demonstrated progression of novel targets and molecules into Phase II clinical trials (e.g. ISM001-055 for IPF), High-throughput synthetic accessibility and automated ADMET filtering | ADMET-AI scientific method, Molecular SMILES with well-defined compound stereochemistry and relevant assay domain, Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Chemical foundation model for learned molecular representations, property prediction and conditional molecule generation., Molecular features, predicted properties or generated SMILES | — |
| Entry type | Commercial Platform | Source-scoped research method | Research AI model / method | — |
| AI role | End-to-End Generative Chemistry | ENSEMBLE_ML | Published biomedical AI method | — |
| Input data | Not recorded | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Valid SMILES; trained downstream endpoint or conditioned molecule task | — |
| Output data | Not recorded | Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Molecular features; predicted properties or generated SMILES | — |
| Validation | Not recorded | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | — |
| Type | End-to-end generative AI drug platform | Not recorded | Not recorded | — |
| Intended use | Not recorded | Not recorded | Not recorded | — |
| Compatibility | Not recorded | Not recorded | Not recorded | — |
| Manufacturer | Insilico Medicine | Not recorded | Not recorded | — |
| Integration evidence | https://insilico.com/platform | Not recorded | Not recorded | — |
| Laboratory handoff | Compounds synthesized in automated robotic labs for biochemical IC50 testing | Not recorded | Not recorded | — |