Ligand biological activity predicted by cleaning positive and negative chemical correlations

By Alpha Albert Lee, Qingyi Yang, Asser Bassyouni, C. Butler, Xinjun Hou, S. Jenkinson, David A. Price. 2019

29 citations recorded by the source corpus.

Significance Predicting ligand biological activity is a key challenge in drug discovery. Although there is an increasing amount of activity data, a data-driven approach needs to overcome the challenge that the number of molecules known to be active or inactive is vastly less than the number of possible chemical features that might determine binding. We develop a framework using random matrix theory that discovers important chemical features by disentangling undersampling noise. This method is used to prospectively discover four experimentally confirmed agonists of the human muscarinic acetylcholine receptor M1, a target for diseases such as Alzheimer’s disease and schizophrenia. Crucially, our method is interpretable and yields prospectively validated chemical insights on the binding modes of the M1 receptor. Predicting ligand biological activity is a key challenge in drug discovery. Ligand-based statistical approaches are often hampered by noise due to undersampling: The number of molecules known to be active or inactive is vastly less than the number of possible chemical features that might determine binding. We derive a statistical framework inspired by random matrix theory and combine the framework with high-quality negative data to discover important chemical differences between active and inactive molecules by disentangling undersampling noise. Our model outperforms standard benchmarks when tested against a set of challenging retrospective tests. We prospectively apply our model to the human muscarinic acetylcholine receptor M1, finding four experimentally confirmed agonists that are chemically dissimilar to all known ligands. The hit rate of our model is significantly higher than the state of the art. Our model can be interpreted and visualized to offer chemical insights about the molecular motifs that are synergistic or antagonistic to M1 agonism, which we have prospectively experimentally verified.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Message passing neural networks for molecular property prediction (2019, methodology): “Some work has focused on building better models for descriptors or fingerprints [32, 27], while other work has explored the use of SMILES [45] strings [32] or the molecular graph [16, 46, 22, 20, 28, 24, 13, 9, 7, 40, 5] as input.”
Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, background): “…2018), (ii) predicting activity based on chemical structure (Granda et al., 2018; Simões et al., 2018; Arús-Pous et al., 2019; Gromski et al., 2019; Lee et al., 2019; Li and Eastgate, 2019), and (iii) selecting promising molecular targets and candidates for further experimental validation (e.g.,…”
Exploring chemical compound space with quantum-based machine learning (2019, methodology): “(f) Identification of (ir)relevant structural motifs for ligand binding [116].”