ReviewCell reports. Medicine2022
Deep generative molecular design reshapes drug discovery.
Review in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.
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
102 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Convergence of disciplines: a systematic review of multidisciplinary development approaches in artificial intelligence.Frontiers in digital health · 2025Pooled it
- The emerging synergy of experimental and computational approaches for therapeutic modulation of biomolecular condensates.SLAS discovery : advancing life sciences R & D · 2026Review
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- TabularQGAN: a quantum generative model for tabular data synthesis.Scientific reports · 2026Article
- Multi-Property De Novo Drug Design Using Deep Learning-Based Knowledge Distillation and Reinforcement Learning.International journal of molecular sciences · 2026Article
- Modular Framework for 3D Molecular Generation in Computational Chemistry Applications.Journal of the American Chemical Society · 2026Article
- Generative Artificial Intelligence Optimization of Albumin Binders: Coumarin and Fatty Acid Derivatives.Journal of chemical information and modeling · 2026Article
- Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer.Molecular diversity · 2026Article
- SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics.Molecular systems biology · 2026Article
- Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- From mechanistic modeling to AI-driven design: computational strategies for targeting the γ-secretase complex.Briefings in bioinformatics · 2026Review
- Globalization in the Healthcare Industry: Drivers, Risks, and Adaptation.Healthcare (Basel, Switzerland) · 2026Review
- Enabling Synthetically Feasible Molecular Editing in Drug Discovery via Reaction-Regulated Graph-Based Genetic Algorithms.JACS Au · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Interaction-constrained 3D molecular generation using a diffusion model enables structure-based pharmacophore modeling for drug design.npj drug discovery · 2026Article
- Multi-View Biomedical Foundation Models for Molecule-Target and Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- De novo covalent drug generation with enhanced drug-likeness and safety.Communications biology · 2026Article
42 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
10 authors.
Funding
Abstract
Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.
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.