ArticleNature communications2024
Automated design of multi-target ligands by generative deep learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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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.
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Who cites it
22 citing papers in PubMed.
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.Current opinion in structural biology · 2026Review
- Design, Synthesis, and Evaluation of Coumarin-Rasagiline Hybrids as Multifunctional Neuroprotective Agents.ChemMedChem · 2026Article
- Phenotypic AI-based design of cell-specific small molecule cytotoxics.Communications chemistry · 2026Article
- MT-ConBiFormer-GPT: multi-target molecular generation for low-data drug discovery via a contrastive BiFormer-GPT architecture and curriculum learning with cross-domain generalization.Briefings in bioinformatics · 2026Article
- Enabling Synthetically Feasible Molecular Editing in Drug Discovery via Reaction-Regulated Graph-Based Genetic Algorithms.JACS Au · 2026Article
- 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
- Structural optimization of drug molecules with incrementally trained language models.Nature communications · 2026Article
- Computational framework to quantify synergistic ligand activity in insulin secretion and resistance pathways in type 2 diabetes.Journal of computer-aided molecular design · 2026Article
- Enabling multi-target drug discovery through latent evolutionary optimization and synthesis-aware prioritization (EVOSYNTH).Communications chemistry · 2026Article
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- Scaffold Fusion and SAR Transfer with a Chemical Language Model Generates Novel Liver X Receptor Modulators.Journal of medicinal chemistry · 2025Article
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- The future of pharmaceuticals: Artificial intelligence in drug discovery and development.Journal of pharmaceutical analysis · 2025Review
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- AI-Driven Polypharmacology in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- ProDualNet: dual-target protein sequence design method based on protein language model and structure model.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical entities with experimentally confirmed activity on intended targets. Here, we probe the application of CLM to generate multi-target ligands for designed polypharmacology. We capitalize on the ability of CLM to learn from small fine-tuning sets of molecules and successfully bias the model towards designing drug-like molecules with similarity to known ligands of target pairs of interest. Designs obtained from CLM after pooled fine-tuning are predicted active on both proteins of interest and comprise pharmacophore elements of ligands for both targets in one molecule. Synthesis and testing of twelve computationally favored CLM designs for six target pairs reveals modulation of at least one intended protein by all selected designs with up to double-digit nanomolar potency and confirms seven compounds as designed dual ligands. These results corroborate CLM for multi-target de novo design as source of innovation in drug discovery.
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What Socratic 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 Socratic graph.