Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations

Schiff, Yair; Chenthamarakshan, Vijil; Hoffman, Samuel C.; Ramamurthy, Karthikeyan Natesan; Das, Payel

doi:10.1109/icassp43922.2022.9747088

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…There remains substantial scope for improving the discovery power of the machine intelligence framework. For example, incorporation of the 3D structural information of the binding pocket, when available, can be used, together with the sequence information, to encode the target (51,52), or the encoding of the residues composing the binding pocket can be used to guide the binder molecule design. However, it is noteworthy that guiding the binder generation by the target sequence information opens other avenues of exploration, such as designing of modulators or designing inhibitors that act via different mechanism.…”

Section: Discussionmentioning

confidence: 99%

Accelerating drug target inhibitor discovery with a deep generative foundation model

et al. 2023

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Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions—unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2. Despite using only the target sequence information during the model inference, micromolar-level inhibition was observed in vitro for two candidates out of four synthesized for each target. The most potent spike RBD inhibitor exhibited activity against several variants in live virus neutralization assays. These results establish that a single, broadly deployable generative foundation model for accelerated inhibitor discovery is effective and efficient, even in the absence of target structure or binder information.

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Section: Discussionmentioning

confidence: 99%

Accelerating drug target inhibitor discovery with a deep generative foundation model

et al. 2023

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“…There remains significant scope for improving the discovery power of the framework: incorporation of the 3D structural information (when available) 45,46 and further constraining the generations (e.g. solubility, number of hydrogen bonding donor/acceptor sites, structural diversity) are potential directions for further work.…”

Section: Discussionmentioning

confidence: 99%

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework

Chenthamarakshan¹,

Hoffman²,

Owen

et al. 2022

Preprint

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The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways. As exhaustive exploration of the vast chemical space is infeasible, discovering novel inhibitor molecules for emerging drug-target proteins is challenging, particularly for targets with unknown structure or ligands. We demonstrate the broad utility of a single deep generative framework toward discovering novel drug-like inhibitor molecules against two distinct SARS-CoV-2 targets — the main protease (Mpro) and the receptor binding domain (RBD) of the spike protein. To perform target-aware design, the framework employs a target sequence-conditioned sampling of novel molecules from a generative model. Micromolar-level in vitro inhibition was observed for two candidates (out of four synthesized) for each target. The most potent spike RBD inhibitor also emerged as a rare non-covalent antiviral with broad-spectrum activity against several SARS-CoV-2 variants in live virus neutralization assays. These results show that a broadly deployable machine intelligence framework can accelerate hit discovery across different emerging drug-targets.

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“…There remains significant scope for improving the discovery power of the framework: incorporation of the 3D structural information (when available) 43,44 and further constraining the generations (e.g. solubility, number of hydrogen bonding donor/acceptor sites, structural diversity) are potential directions for further work.…”

Section: Discussionmentioning

confidence: 99%

Accelerating Inhibitor Discovery With A Deep Generative Foundation Model: Validation for SARS-CoV-2 Drug Targets

Chenthamarakshan¹,

Hoffman²,

Owen³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways. As exhaustive exploration of the vast chemical space is infeasible, discovering novel inhibitor molecules for emerging drug-target proteins is challenging, particularly for targets with unknown structure or ligands. We demonstrate the broad utility of a single deep generative framework toward discovering novel drug-like inhibitor molecules against two distinct SARS-CoV-2 targets -the main protease (M pro ) and the receptor binding domain (RBD) of the spike protein. To perform target-aware design, the framework employs a target sequence-conditioned sampling of novel molecules from a generative model. Micromolar-level in vitro inhibition was observed for two candidates (out of four synthesized) for each target. The most potent spike RBD inhibitor also emerged as a rare non-covalent antiviral with broad-spectrum activity against several SARS-CoV-2 variants in live virus neutralization assays. These results show that a broadly deployable machine intelligence framework can accelerate hit discovery across different emerging drug-targets.

show abstract

Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations

Cited by 8 publications

References 17 publications

Accelerating drug target inhibitor discovery with a deep generative foundation model

Accelerating drug target inhibitor discovery with a deep generative foundation model

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework

Accelerating Inhibitor Discovery With A Deep Generative Foundation Model: Validation for SARS-CoV-2 Drug Targets

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