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将生物大模型、对接计算引擎与组学管线并排横向对比。按获取方式、硬件开销及细分领域灵活筛选，在一分钟内得出客观可辩护的技术选型方案。
| 技术参数指标 | ＋ 添加工具 | |
|---|---|---|
| Category | AI Drug Discovery | — |
| Subcategory | Molecular Docking | — |
| Company | EquiBind authors | — |
| Access mode | Public code / project terms | — |
| Licence conditions | Review the linked release, model weights, datasets and service terms separately | — |
| Compute requirements | Consult linked installation guidance; workload dependent | — |
| Biological application | 未明确记录 | — |
| Research workflow | 未明确记录 | — |
| Evidence level | Project or provider documentation; no local performance benchmark | — |
| Commercial eligibility | Not independently confirmed | — |
| Price | See project terms; compute costs may apply | — |
| Availability ⓘ | See project access and maintenance status | — |
| Features | Geometric deep-learning prediction of ligand binding poses, Input: Protein and small-molecule structures, Output: Predicted bound ligand coordinates | — |
| Entry type | Research software / model | — |
| AI role | Scientific machine learning | — |
| Input data | Protein and small-molecule structures | — |
| Output data | Predicted bound ligand coordinates | — |
| Validation | Evaluate on representative data; no clinical suitability established | — |
| Type | 未明确记录 | — |
| Intended use | Biological and pharmaceutical research | — |
| Compatibility | 未明确记录 | — |
| Limitations | Assess steric plausibility and target-specific performance before using poses downstream. | — |