A Morse Transform for Drug Discovery
This provides an efficient and interpretable solution for early-stage drug discovery, addressing a domain-specific bottleneck in ligand-based virtual screening.
The paper tackles the problem of predicting ligand binding potential in drug discovery by introducing a new framework based on piecewise linear Morse theory, achieving state-of-the-art performance on standard datasets with an interpretable and scalable method.
We introduce a new ligand-based virtual screening (LBVS) framework that uses piecewise linear (PL) Morse theory to predict ligand binding potential. We model ligands as simplicial complexes via a pruned Delaunay triangulation, and catalogue the critical points across multiple directional height functions. This produces a rich feature vector, consisting of crucial topological features -- peaks, troughs, and saddles -- that characterise ligand surfaces relevant to binding interactions. Unlike contemporary LBVS methods that rely on computationally-intensive deep neural networks, we require only a lightweight classifier. The Morse theoretic approach achieves state-of-the-art performance on standard datasets while offering an interpretable feature vector and scalable method for ligand prioritization in early-stage drug discovery.