ArticleBriefings in bioinformatics2024
A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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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.
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Who cites it
35 citing papers in PubMed.
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- Accelerating the clinical translation of bioengineered anticancer therapeutics.Journal of the National Cancer Center · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- Repurposing apremilast for alzheimer's disease: multitarget modulation of cAMP‑PI3K/Akt-GSK‑3β and NF‑κB signaling.Metabolic brain disease · 2026Review
- Fitness translocation: improving variant effect prediction with biologically-grounded data augmentation.Bioinformatics (Oxford, England) · 2026Article
- Pep2Mol: 3D Molecule Generation Targeting Protein-Protein Interfaces with Diffusion Models.bioRxiv : the preprint server for biology · 2026Article
- Impact of molecular multimodality on neural network models for prediction tasks related to drug discovery.Nature communications · 2026Article
- Prime editing updates: technological evolution, methodological expansion, and delivery strategies for in vivo applications.BMB reports · 2026Review
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation.Journal of orthopaedic translation · 2026Review
- A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations.Journal of chemical information and modeling · 2026Article
- NaviDiv: a web app for monitoring chemical diversity in generative molecular design.Digital discovery · 2026Article
- Enabling multi-target drug discovery through latent evolutionary optimization and synthesis-aware prioritization (EVOSYNTH).Communications chemistry · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- De novo protein-ligand design including protein flexibility and conformational adaptation.Bioinformatics (Oxford, England) · 2026Article
- MolOrgGPT:Journal of chemical information and modeling · 2026Article
- Protein design, generative AI and biological security.Frontiers in microbiology · 2026Review
- Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Generative Chemistry Platform for Small Molecules Targeting RNA: A Case Study for Chemical Optimization.Computational and structural biotechnology journal · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.
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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.