ArticleJournal of cheminformatics2023
Deep generative model for drug design from protein target sequence.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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The trial behind it
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Who cites it
25 citing papers in PubMed.
- An interpretable framework applying protein words to predict protein-small molecule complementary pairing rules.Chemical science · 2026Article
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- A multi-target drug design method based on target feature fusion.BMC bioinformatics · 2026Article
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- All-atom protein sequence design using discrete diffusion models.Journal of cheminformatics · 2025Article
- AgentMol: Multi-Model AI System for Automatic Drug-Target Identification and Molecule Development.Methods and protocols · 2025Article
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- ZeroGEN: leveraging language models for zero-shot ligand design from protein sequences.Bioinformatics (Oxford, England) · 2025Article
- Optimizing drug design by merging generative AI with a physics-based active learning framework.Communications chemistry · 2025Article
- Targeting Ferroptosis to Eliminate Senescent Cells: Mechanisms and Therapeutic Potential.Aging and disease · 2025Review
- Hybridization of SMILES and chemical-environment-aware tokens to improve performance of molecular structure generation.Scientific reports · 2025Article
- A structure-based framework for selective inhibitor design and optimization.Communications biology · 2025Article
- CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.Journal of cheminformatics · 2025Article
- Transformer Decoder Learns from a Pretrained Protein Language Model to Generate Ligands with High Affinity.Journal of chemical information and modeling · 2025Article
- Deep learning-based design and experimental validation of a medicine-like human antibody library.Briefings in bioinformatics · 2024Article
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.Proteins · 2024Article
- Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits.Journal of cheminformatics · 2024Article
- Inverse mapping of quantum properties to structures for chemical space of small organic molecules.Nature communications · 2024Article
- HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction.Journal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Drug discovery for a protein target is a laborious and costly process. Deep learning (DL) methods have been applied to drug discovery and successfully generated novel molecular structures, and they can substantially reduce development time and costs. However, most of them rely on prior knowledge, either by drawing on the structure and properties of known molecules to generate similar candidate molecules or extracting information on the binding sites of protein pockets to obtain molecules that can bind to them. In this paper, DeepTarget, an end-to-end DL model, was proposed to generate novel molecules solely relying on the amino acid sequence of the target protein to reduce the heavy reliance on prior knowledge. DeepTarget includes three modules: Amino Acid Sequence Embedding (AASE), Structural Feature Inference (SFI), and Molecule Generation (MG). AASE generates embeddings from the amino acid sequence of the target protein. SFI inferences the potential structural features of the synthesized molecule, and MG seeks to construct the eventual molecule. The validity of the generated molecules was demonstrated by a benchmark platform of molecular generation models. The interaction between the generated molecules and the target proteins was also verified on the basis of two metrics, drug-target affinity and molecular docking. The results of the experiments indicated the efficacy of the model for direct molecule generation solely conditioned on amino acid sequence.
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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.