Shape-Based Generative Modeling for de Novo Drug Design

By Miha Škalič, J. Jiménez, Davide Sabbadin, G. de Fabritiis. 2019

208 citations recorded by the source corpus.

In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image analysis and represents a first example of the de novo design of lead-like molecules guided by shape-based features. A variational autoencoder is used to perturb the 3D representation of a compound, followed by a system of convolutional and recurrent neural networks that generate a sequence of SMILES tokens. The generative design of novel scaffolds and functional groups can cover unexplored regions of chemical space that still possess lead-like properties.

Subfields: Biophysics, Computational Theory and Mathematics, Materials Chemistry

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Citations in the corpus

3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023, methodology): “…progress made in this direction, the models still suffer from several issues, including separately encoding the small molecules and protein pockets (Skalic et al., 2019b; Xu et al., 2021; Tan et al., 2022; Ragoza et al., 2022), relying on voxelization and non-equivariance networks (Skalic et al.,…”
A 3D Generative Model for Structure-Based Drug Design (2022, background): “Another line of work studies generating molecules directly in 3D space [8, 28, 29, 20, 30, 15].”
Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models (2020, background): “This is a multiplier of the generated variance [38].”
Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “Recently, instances of conditional generative approaches have been suggested, which leverage additional information guiding the design, such as three-dimensional shape [144], drug-likeness [142], synthesizability [142,145], molecular descriptors values [146], and gene expression signatures [147].”