A Morse Transform for Drug Discovery
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909527935287296 |
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| author | Tanaka, Alexander M. Asaad, Aras T. Cooper, Richard Nanda, Vidit |
| author_facet | Tanaka, Alexander M. Asaad, Aras T. Cooper, Richard Nanda, Vidit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04507 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Morse Transform for Drug Discovery Tanaka, Alexander M. Asaad, Aras T. Cooper, Richard Nanda, Vidit Quantitative Methods Machine Learning 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. |
| title | A Morse Transform for Drug Discovery |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2503.04507 |