Deep generative design with 3D pharmacophoric constraints

By F. Imrie, Thomas E. Hadfield, Anthony R. Bradley, Charlotte M. Deane. 2021

85 citations recorded by the source corpus.

Generative models have increasingly been proposed as a solution to the molecular design problem. However, it has proved challenging to control the design process or incorporate prior knowledge, limiting their practical use in drug discovery. In particular, generative methods have made limited use of three-dimensional (3D) structural information even though this is critical to binding. This work describes a method to incorporate such information and demonstrates the benefit of doing so. We combine an existing graph-based deep generative model, DeLinker, with a convolutional neural network to utilise physically-meaningful 3D representations of molecules and target pharmacophores. We apply our model, DEVELOP, to both linker and R-group design, demonstrating its suitability for both hit-to-lead and lead optimisation. The 3D pharmacophoric information results in improved generation and allows greater control of the design process. In multiple large-scale evaluations, we show that including 3D pharmacophoric constraints results in substantial improvements in the quality of generated molecules. On a challenging test set derived from PDBbind, our model improves the proportion of generated molecules with high 3D similarity to the original molecule by over 300%. In addition, DEVELOP recovers 10 × more of the original molecules compared to the base-line DeLinker method. Our approach is general-purpose, readily modifiable to alternate 3D representations, and can be incorporated into other generative frameworks. Code is available at https://github.com/oxpig/DEVELOP.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Pharmacology

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Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “, 2020) or de novo binder generation (Masuda et al., 2020; Imrie et al., 2021; Drotár et al., 2021).”
Equivariant 3D-conditional diffusion model for molecular linker design (2022, unknown): “[23] overcome these limitations by introducing an autoregressive model DeLinker and its extension DEVELOP [24] that uses additional pharmacophore information.”