ArticleNature communications2023
Sequence-based drug design as a concept in computational drug design.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
What it found
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Who cites it
62 citing papers in PubMed, 111 citations in OpenAlex.
- The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists.npj drug discovery · 2026Review
- Targeting the FOXP3-T-bet interaction to restore Treg stability in IFN-γ-driven autoimmunity.Cellular & molecular immunology · 2026Article
- An interpretable framework applying protein words to predict protein-small molecule complementary pairing rules.Chemical science · 2026Article
- Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DeepExoMir: A Reproducible RNA Language Model Framework for CLIP-Seq-Supported MicroRNA Target-Site Prioritization.International journal of molecular sciences · 2026Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Unveiling the bioactive landscape of drug inactive ingredients (DIGs) using deep transfer learning.Acta pharmaceutica Sinica. B · 2026Article
- Chemical adaptation: bridging synthetic chemistry with drug development.Science China. Life sciences · 2026Review
- Bridging antiviral drug discovery with a large language model-powered framework.Communications biology · 2026Article
- AI platform for CRISPR functional mapping and function-based drug design.bioRxiv : the preprint server for biology · 2026Article
- Structural optimization of drug molecules with incrementally trained language models.Nature communications · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Antiviral drug discovery and development: challenges and future directions.Signal transduction and targeted therapy · 2026Review
- Deciphering RNA and protein expression discordance identifies TOP2A as a prognostic biomarker and potential therapeutic target in lung adenocarcinoma.Discover oncology · 2026Article
- [Application and progress of artificial intelligence agents in drug development].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2026Review
- Transformer Learning in Sequence-Based Drug Design Depends on Compound Memorization and Similarity of Sequence-Compound Pairs.Molecular informatics · 2026Review
- Identifying potential inflammatory therapeutic targets and drug candidates in small fiber neuropathy: integrating Mendelian randomization, experimental validation, and deep learning.Frontiers in neuroscience · 2026Article
- Biological sequence analysis: Advances, medical applications, and challenges.Fundamental research · 2026Review
- Multimodal pre-training models of molecular representation for drug discovery.National science review · 2026Review
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.
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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.