arXiv:2509.00704v2 Announce Type: replace Abstract: The scalability of pool-based active learning is limited by the computational cost of evaluating large unlabeled datasets, a challenge that is particularly acute in virtual screening for drug discovery. While active learning strategies such as Bayesian Active Learning by Disagreement (BALD) prioritize informative samples, it remains computationally intensive when scaled to libraries containing billions samples. In this work, we introduce BALD-GFlowNet, a generative active learning framework that circumvents this issue. Our method leverages Ge
The increasing scale of biological and chemical data sets, combined with the computational demands of existing active learning methods, necessitates innovative approaches to accelerate drug discovery and material science.
This development proposes a method to significantly reduce the computational cost of active learning in drug discovery and similar fields, potentially accelerating the development timelines for new therapeutics and materials.
The paradigm for high-throughput virtual screening could shift from 'pool-based' evaluation of massive datasets to 'flow-based' generative methods, identifying promising candidates more efficiently.
- · Pharmaceutical companies
- · Biotech firms
- · AI/ML drug discovery platforms
- · Researchers in synthetic biology
- · Traditional drug screening methods
- · Companies reliant on brute-force computational screening
Faster identification of candidate molecules for drug development and materials science.
Reduced R&D costs and shortened time-to-market for new drugs and materials.
Accelerated advancements in personalized medicine and novel material discovery, impacting health and industrial sectors globally.
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