De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

By Oscar Méndez-Lucio, B. Baillif, Djork-Arné Clevert, D. Rouquié, J. Wichard. Nature Communications, 2018

343 citations recorded by the source corpus.

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery. High quality hit identification remains a considerable challenge in de novo drug design. Here, the authors train a generative adversarial network with transcriptome profiles induced by a large set of compounds, enabling it to design molecules that are likely to induce desired expression profiles.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Pharmacology

View Paper: https://www.semanticscholar.org/paper/d8498da01ce27e17f58adb73a2d782d6af44884d

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Citations in the corpus

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].”
Artificial intelligence in drug development (2025, unknown): “Beyond introducing scoring functions, incorporating constraints— such as disease-related gene expression features 109 , pharmacophores 110 , protein sequences 87 or structures 102,111–114 , binding affinity 115 and protein–ligand interactions 116,117 —can also direct models toward generating…”
Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models (2020, unknown): “These have been successfully applied to generating molecules that have desired molecular properties or related gene expressions[28, 29].”