ArticleNature communications2020
De novo generation of hit-like molecules from gene expression signatures using artificial intelligence.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers.
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Who cites it
118 citing papers in PubMed.
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- GEM-GPT Enables Personalized Cell Type-Resolved Therapeutic Design for Systems Pharmacology.bioRxiv : the preprint server for biology · 2026Article
- AET5: A transcriptome-guided molecular generation framework with contrastive self-supervised learning.PLoS computational biology · 2026Article
- ZIF-Derived FeCo Nanoparticles and FeNChemSusChem · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning.Bioinformatics (Oxford, England) · 2026Article
- Evaluating AI-Generated Molecules for Drug Discovery: From Generic Metrics to Translational Readiness.International journal of molecular sciences · 2026Review
- Phenotypic AI-based design of cell-specific small molecule cytotoxics.Communications chemistry · 2026Article
- Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop.Archives of toxicology · 2026Article
- Navigating condensate micropolarity to enhance small-molecule drug targeting.Nature chemical biology · 2026Article
- MolMod: a molecular modification platform for molecular property optimization via fragment-based generation.Molecular diversity · 2026Article
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- Generative Chemistry Platform for Small Molecules Targeting RNA: A Case Study for Chemical Optimization.Computational and structural biotechnology journal · 2026Article
- Discovering TEAD4 modulators for hepatocellular carcinoma: a GAN-enabled generative modelling framework.Frontiers in bioinformatics · 2026Article
- SIMD: Synergistic integration mutualistic platform based on single-cell and proteotranscriptomics for drug repositioning.NPJ breast cancer · 2025Article
- Reinforcement learning-based generative artificial intelligence for novel pesticide design.Journal of advanced research · 2025Article
- Transcriptome-conditioned molecule generation via gene interaction-aware fragment modeling with a GPT-based architecture.Scientific reports · 2025Article
- MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models.Journal of chemical information and modeling · 2025Article
- Structure-aware diffusion model for molecule generation based on K-Nearest Neighbor and equivariant graph neural network.Future medicinal chemistry · 2025Article
- Big data and AI: Potential and challenges for digital transformation in toxicology.Environmental analysis, health and toxicology · 2025Article
58 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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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.