# AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach

arXiv: https://arxiv.org/abs/2002.05635 · HTML: https://mlsanity.com/paper/2002.05635/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 21 of 21)

- [ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models](https://mlsanity.com/paper/2404.07738/) (2024)
- [SciMON: Scientific Inspiration Machines Optimized for Novelty](https://mlsanity.com/paper/2305.14259/) (2023)
- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders](https://mlsanity.com/paper/2405.17044/) (2024)
- [IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery](https://mlsanity.com/paper/2504.16728/) (2025)
- [How Far Are AI Scientists from Changing the World?](https://mlsanity.com/paper/2507.23276/) (2025)
- [HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance](https://mlsanity.com/paper/2506.12937/) (2025)
- [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024)
- [BioDisco: Multi-agent hypothesis generation with dual-mode evidence, iterative feedback and temporal evaluation](https://mlsanity.com/paper/2508.01285/) (2025)
- [BioVerge: A Comprehensive Benchmark and Study of Self-Evaluating Agents for Biomedical Hypothesis Generation](https://mlsanity.com/paper/2511.08866/) (2025)
- [Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining](https://mlsanity.com/paper/2310.03766/) (2023)
- [Accelerating COVID-19 research with graph mining and transformer-based learning](https://mlsanity.com/paper/2102.07631/) (2021)
- [Towards Practical Explainability with Cluster Descriptors](https://mlsanity.com/paper/2210.10662/) (2022)
- [Literature-based Discovery for Landscape Planning](https://mlsanity.com/paper/2306.02588/) (2023)
- [Graphs of Research: Citation Evolution Graphs as Supervision for Research Idea Generation](https://mlsanity.com/paper/2605.14790/) (2026)
- [GreedyML: A Parallel Algorithm for Maximizing Constrained Submodular Functions](https://mlsanity.com/paper/2403.10332/) (2024)
- [Biomedical Hypothesis Explainability with Graph-Based Context Retrieval](https://mlsanity.com/paper/2511.05498/) (2025)
- [From Literature to Hypotheses: An AI Co-Scientist System for Biomarker-Guided Drug Combination Hypothesis Generation](https://mlsanity.com/paper/2603.00612/) (2026)
- [Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine](https://mlsanity.com/paper/2605.18144/) (2026)
- [HyGRAIL: Cost-Aware and Evidence-Grounded Scientific Hypothesis Discovery over Knowledge Graphs](https://mlsanity.com/paper/2609.02056/) (2026)
- [SCoR: A Hierarchical Framework for Forecasting Relations Between Scientific Concepts](https://mlsanity.com/paper/2609.22174/) (2026)

## Abstract (arXiv)

Medical research is risky and expensive. Drug discovery, as an example, requires that researchers efficiently winnow thousands of potential targets to a small candidate set for more thorough evaluation. However, research groups spend significant time and money to perform the experiments necessary to determine this candidate set long before seeing intermediate results. Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions. We present AGATHA, a deep-learning hypothesis generation system that can introduce data-driven insights earlier in the discovery process. Through a learned ranking criteria, this system quickly prioritizes plausible term-pairs among entity sets, allowing us to recommend new research directions. We massively validate our system with a temporal holdout wherein we predict connections first introduced after 2015 using data published beforehand. We additionally explore biomedical sub-domains, and demonstrate AGATHA's predictive capacity across the twenty most popular relationship types. This system achieves best-in-class performance on an established benchmark, and demonstrates high recommendation scores across subdomains. Reproducibility: All code, experimental data, and pre-trained models are available online: sybrandt.com/2020/agatha