ArticleMolecular informatics2018
Generative Recurrent Networks for De Novo Drug Design.
Article in Molecular informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 159 papers, 1 of them a synthesis that pooled it.
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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
159 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.International journal of molecular sciences · 2023Pooled it
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- Ink to innovation: Pioneering printing technologies for next-generation drug delivery systems.Drug delivery and translational research · 2026Review
- SmileyLlama: modifying large language models for directed chemical space exploration.Nature computational science · 2026Article
- Generative AI-Based Drug Design: Target-Aware Molecular Generation for EGFR Type I Inhibitors with Multiplatform Docking Validation.ACS omega · 2026Article
- Novel molecular design via a scaffold-aware transformer with multi-scale attention mechanisms.Journal of cheminformatics · 2026Article
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- LigGen-a GEN-AI based ligand generation approach for de-novo drug design.Scientific reports · 2026Article
- Structural optimization of drug molecules with incrementally trained language models.Nature communications · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- De novo covalent drug generation with enhanced drug-likeness and safety.Communications biology · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- From Latent Manifolds to Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Based Kinase Ligand Design.Biomolecules · 2026Article
- AI-drivenPeerJ · 2026Article
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
99 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets.
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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.