SPADE: Faster Drug Discovery by Learning from Sparse Data

TL;DR

SPADE is a novel algorithm that significantly accelerates drug discovery by efficiently identifying high-quality ligands with minimal testing, outperforming existing methods in speed and sample efficiency.

Contribution

Introduces SPADE, a new ligand selection algorithm that requires fewer tests and outperforms deep learning and Bayesian optimization methods in drug discovery.

Findings

SPADE requires only 40 tests on average to find 10 high-quality ligands.

SPADE outperforms deep learning and Bayesian optimization in sample efficiency by 7%-32%.

SPADE is 10x faster than closest competitors in scoring candidate drugs.

Abstract

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