ReviewJournal of molecular modeling2021
Generative chemistry: drug discovery with deep learning generative models.
Review in Journal of molecular modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed.
- Liver Disease and Plant-Derived Phytoconstituents: From Ethnopharmacology to Modern Medicine.Phytotherapy research : PTR · 2026Review
- Exploiting the T790M Gatekeeper: A Theoretical Blueprint for Non-Covalent Inhibition of inPharmaceutics · 2026Article
- SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics.Molecular systems biology · 2026Article
- Article
- MolVE: An Open-Source Web Platform for Visualizing and Evaluating AI-Designed Molecules to Aid in Prioritization.Journal of chemical information and modeling · 2026Article
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- MolGAN-QRL: a hybrid framework for molecule generation using quantum-enhanced reinforcement learning.Journal of cheminformatics · 2026Article
- From Latent Manifolds to Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Based Kinase Ligand Design.Biomolecules · 2026Article
- Mining the Hidden Pharmacopeia: Fungal Endophytes, Natural Products, and the Rise of AI-Driven Drug Discovery.International journal of molecular sciences · 2026Review
- Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA NNature communications · 2026Article
- Generative Chemistry Platform for Small Molecules Targeting RNA: A Case Study for Chemical Optimization.Computational and structural biotechnology journal · 2026Article
- Bidirectional reinforcement learning neural network for constrained molecular design.Scientific reports · 2025Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Theranostics 2.0: Target-driven, artificial intelligence-enabled cancer therapy across tumor types.Diagnostic and interventional imaging · 2025Review
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
- A generative framework for enhancing drug target interaction prediction in drug discovery.Scientific reports · 2025Article
- BInD: Bond and Interaction-Generating Diffusion Model for Multi-Objective Structure-Based Drug Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Revolutionizing multi-omics analysis with artificial intelligence and data processing.Quantitative biology (Beijing, China) · 2025Review
- Optimizing drug design by merging generative AI with a physics-based active learning framework.Communications chemistry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.