ArticleJournal of cheminformatics2024
Reinvent 4: Modern AI-driven generative molecule design.
Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 142 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
142 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antisense Versus Antigene in the Computer-Aided Design of Triplex-Forming Oligonucleotides (TFO): Insights from a Dual-Method Review, Combining Bibliometric and Systematic Review.International journal of molecular sciences · 2025Pooled it
- Evaluating Machine Learning Affinity Prediction Methods on Novel Lead Optimization Data: A 15-PGDH Case Study.ChemMedChem · 2026Article
- The pan-African natural products library compounds oleanolic acid, poinsettifolin B, and rhuschalcone III disrupt SARS-CoV-2 spike-host ACE2 interactions and SARS-CoV-2 replication.BMC pharmacology & toxicology · 2026Article
- Hierarchical generative modeling for the design of multi-component systems.Journal of cheminformatics · 2026Article
- A critical appraisal of structure-based virtual screening, binding free energy methods, and AI-driven molecular design across the evolving computational landscape of drug discovery.Journal of computer-aided molecular design · 2026Review
- Multi-objective-conditioned-return-blocks decision transformer: a novel approach for multi-objective de novo drug design.Bioinformatics (Oxford, England) · 2026Article
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- New thinking for the next generation of antimalarials.EMBO molecular medicine · 2026Review
- Multiobjective Fluorescent Molecule Design with a Data-Physics Dual-Driven Generative Framework.JACS Au · 2026Article
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- Predicting molecular structure from sparse NMR data, proof-of-concept using a graph neural network.Digital discovery · 2026Article
- µORScreen: a lightweight consensus modeling framework for µ-opioid receptor ligand prediction and virtual screening.RSC advances · 2026Article
- PockLigGPT: Pocket-Sequence-Conditioned Molecular Generation with GPTs and RL.Journal of chemical information and modeling · 2026Article
- Controllable molecular generation with fine-tuned flow-matching model.Communications chemistry · 2026Article
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- Entering the agentic era of AI in drug discovery.Nature chemical biology · 2026Article
- The impact of reward scalarization and weight scheduling on optimization dynamics in multi-objective molecular design.Journal of cheminformatics · 2026Article
- Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Prediction.Journal of chemical theory and computation · 2026Article
- AI-Based Retrosynthesis of Known and Unexplored Covalent Organic Framework Monomers.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- EasyDock 1.3: An Automated Pipeline for Molecular Docking.Journal of chemical information and modeling · 2026Article
82 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Scientific contribution. The software provides an open-source reference implementation for generative molecular design where the software is also being used in production to support in-house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education.
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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.