ArticleScience advances2023
Accelerating drug target inhibitor discovery with a deep generative foundation model.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- The use of artificial intelligence (AI) in neuropsychiatric drug discovery: current challenges and future directions.Translational psychiatry · 2026Review
- The cocrystal advantage: overcoming polymorph patent barriers in generic drug development.Molecular diversity · 2026Review
- A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era.Journal of chemical theory and computation · 2026Review
- The evolving global landscape of first-in-class oncology drug innovation.Signal transduction and targeted therapy · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery.International journal of molecular sciences · 2026Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Antiviral drug discovery and development: challenges and future directions.Signal transduction and targeted therapy · 2026Review
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- The tryptophan-kynurenine pathway in cardiovascular diseases: mechanistic insights and therapeutic opportunities.Frontiers in cardiovascular medicine · 2026Review
- druglikeFilter 1.0: An AI powered filter for collectively measuring the drug-likeness of compounds.Journal of pharmaceutical analysis · 2025Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- Accelerating discovery of bioactive ligands with pharmacophore-informed generative models.Nature communications · 2025Article
- Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model.Science advances · 2025Article
- Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease.Digital discovery · 2025Article
- Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance.npj antimicrobials and resistance · 2025Review
- Artificial intelligence in drug development.Nature medicine · 2025Review
- Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning forRSC medicinal chemistry · 2024Article
- Targeting the Main Protease (MACS chemical biology · 2024Article
- How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors.Biomedicines · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.