SPADE: Faster Drug Discovery by Learning from Sparse Data
For drug discovery researchers, SPADE offers a faster and more sample-efficient method to identify high-quality ligands for novel proteins.
SPADE is a novel ligand selection algorithm that finds 10 high-quality ligands in only 40 tests on average, outperforming deep learning and Bayesian optimization methods with 7%-32% median improvement in sample efficiency and 10x faster scoring.
Drug discovery seeks molecules (ligands) that bind strongly and selectively to a target protein. However, fewer than 5% of candidate ligands pass the bar for even the early stages of drug discovery. Furthermore, we want methods that work for novel proteins for which we have no prior data. Starting from scratch, we have to iteratively select and test candidate ligands such that we find enough ligands of the desired quality in as few tests as possible. Our proposed algorithm, named SPADE, introduces a novel approach to ligand selection that requires only 40 tests on average to find 10 high-quality ligands. In one-vs-one comparisons, SPADE outperforms deep learning and Bayesian optimization methods on more proteins, achieving median improvements of 7%-32% in sample efficiency. SPADE is also 10x faster than its closest competitor at scoring candidate drugs. Dataset and code is available at https://anonymous.4open.science/r/SPADE_Fast_Drug_Discovery_by_Learning_from_Sparse_Data-F028/README.md