Inverse design of 3d molecular structures with conditional generative neural networks

By Niklas W. A. Gebauer, Michael Gastegger, Stefaan Simon Pierre Hessmann, Klaus‐Robert Müller , Kristof T. Schütt. 2021

228 citations recorded by the source corpus.

The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime. The targeted discovery of molecules with specific structural and chemical properties is an open challenge in computational chemistry. Here, the authors propose a conditional generative neural network for the inverse design of 3d molecular structures.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation (2022, background): “The 3D structures determine the biological and physical properties of molecules and hence play a key role in many applications such as computational drug and material de-sign (Thomas et al., 2018; Gebauer et al., 2021; Jing et al., 2021; Batzner et al., 2021).”
Equivariant Diffusion for Molecule Generation in 3D (2022, methodology): “Some recent methods directly generate molecules in 3D: (Gebauer et al., 2019; Luo & Ji, 2021; Luo et al., 2021a; Gebauer et al., 2021) deﬁne an order-dependent autoregressive distribution from which atoms are iteratively sampled.”
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design (2022, unknown): “3D-Scaffold [186] 3D-Geometry 3D-Geometry AR cG-SchNet [187] 3D-Geometry 3D-Geometry AR”
Artificial intelligence in drug development (2025, unknown): “Autoregressive approaches construct molecules atom by atom, which can lead to chemically implausible intermediates and introduce bias 101,102 .”
Crystal Diffusion Variational Autoencoder for Periodic Material Generation (2021, background): “(6)Very recently the authors published an improved version for conditional generation (Gebauer et al., 2021) but the code has not been released yet.”