mlsanityData to 14 Oct 2026

GeneDisco: A Benchmark for Experimental Design in Drug Discovery

arXiv 2110.11875 ↗First posted 22 Oct 2021cs.LG stat.MLcited by 18

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Abstract (arXiv)

In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that serves to assess initial hypotheses about causal associations between biological mechanisms and disease pathologies. With billions of potential hypotheses to test, the experimental design space for in vitro genetic experiments is extremely vast, and the available experimental capacity - even at the largest research institutions in the world - pales in relation to the size of this biological hypothesis space. Machine learning methods, such as active and reinforcement learning, could aid in optimally exploring the vast biological space by integrating prior knowledge from various information sources as well as extrapolating to yet unexplored areas of the experimental design space based on available data. However, there exist no standardised benchmarks and data sets for this challenging task and little research has been conducted in this area to date. Here, we introduce GeneDisco, a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery. GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 28 of its 99 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

28 of 99 references.

References · 28

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16 more
  1. 2020CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learningcited by 109
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Citing papers per year: 2022: 7; 2023: 3; 2024: 3; 2025: 4; 2026: 1.

18 papers.

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  1. 2024BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experimentscited by 53
  2. 2022A Framework and Benchmark for Deep Batch Active Learning for Regressioncited by 49
  3. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  4. 2024Active Learning for Neural PDE Solverscited by 22
  5. 2023DRCFS: Doubly Robust Causal Feature Selectioncited by 12
  6. 2023DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Designcited by 12
  7. 2024Automated Discovery of Pairwise Interactions from Unstructured Datacited by 8
  8. 2022Neural Design for Genetic Perturbation Experimentscited by 6
  9. 2022Trust Your nabla: Gradient-based Intervention Targeting for Causal Discoverycited by 5
  10. 2025In-silico biological discovery with large perturbation modelscited by 4
  11. 2022Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networkscited by 3
  12. 2023Multi-omics Prediction from High-content Cellular Imaging with Deep Learningcited by 3
6 more
  1. 2022PyRelationAL: a python library for active learning research and developmentcited by 2
  2. 2025BioBO: Biology-informed Bayesian Optimization for Perturbation Designcited by 2
  3. 2022Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selectioncited by 0
  4. 2025Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeuticscited by 0
  5. 2025Efficient Data Selection for Training Genomic Perturbation Modelscited by 0
  6. 2026Many Needles in a Haystack: Active Hit Discovery for Perturbation Experimentscited by 0