ArticleChemical science2024
Sample efficient reinforcement learning with active learning for molecular design.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Reinforcement Learning for Cathode Material Design Through Sequential Decision-Making Frameworks.Nanomaterials (Basel, Switzerland) · 2026Review
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- Improving hit discovery by integrating activity cliff sensitivity into active learning.Bioinformatics (Oxford, England) · 2026Article
- SynthesizabilityChemical science · 2026Article
- Quantum Chemistry-Driven Molecular Inverse Design of Stable Isomers with Data-Free Reinforcement Learning.Journal of chemical theory and computation · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- A unified active learning framework for photosensitizer design.Chemical science · 2026Article
- Generative Multiobjective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design.Industrial & engineering chemistry research · 2026Article
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model.Angewandte Chemie (International ed. in English) · 2026Article
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Bidirectional reinforcement learning neural network for constrained molecular design.Scientific reports · 2025Article
- Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- Identification of nanomolar adenosine ANature communications · 2025Article
- Nanotechnology-Driven Drug Delivery Systems for Lung Cancer: Computational Advances and Clinical Perspectives.Thoracic cancer · 2025Review
- Artificial intelligence in drug development.Nature medicine · 2025Review
- Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning.JACS Au · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual laboratory experiments) requires improvements in terms of sample efficiency to make the most of expensive information. The discovery of new drugs is a major commercial application of RL, motivated by the very large nature of the chemical space and the need to perform multiparameter optimization (MPO) across different properties.
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.