ICML 2025

Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations
Jason Qin 1 Hans-Hermann Wessels 1 Carlos Fernandez-Granda 2 3 Yuhan Hao 1
Abstract
Combinatorial CRISPR screening enables large-
scale identification of synergistic gene pairs for
combination therapies, but exhaustive experimen-
tation is infeasible. We introduce NAIAD1, an ac-
tive learning framework that efficiently discovers
optimal gene pairs by leveraging single-gene per-
turbation effects and adaptive gene embeddings
that scale with the training data size, mitigating
overfitting in small-sample learning while cap-
turing complex gene interactions as more data is
collected. Evaluated on four CRISPR datasets
with over 350,000 interactions, NAIAD trained
on small datasets outperforms existing models by
up to 40%. Its recommendation system priori-
tizes gene pairs with maximum predicted effects,
accelerating discovery with fewer experiments.
We also extend NAIAD to optimal drug com-
bination identification among 2,000 candidates.
Overall, NAIAD enhances combinatorial pertur-
bation design and drives advances in genomics
research and therapeutic development in combi-
nation therapy. Our code is publicly available at:
https://github.com/NeptuneBio/NAIAD
1. Introduction
Targeting multiple genes through drug combinations or
polypharmacology offers a transformative therapeutic ap-
proach for developing effective treatments across diverse
medical fields, including oncology (Al-Lazikani et al., 2012;
Mokhtari et al., 2017), infectious diseases (Hammond et al.,
2022; Shyr et al., 2021), and metabolic disorders (Samms
et al., 2020; Jastreboff et al., 2023). Combinatorial gene per-
turbations can yield additive or synergistic effects, enhanc-
ing therapeutic outcomes beyond what is achievable with
1Neptune Bio, New York, NY, USA 2Center for Data Science,
New York University, New York, NY, USA 3Courant Institute
of Mathematical Sciences, New York University, New York, NY,
USA . Correspondence to:...